“image acquisition and manipulation protocols for ct-pet fusion … · 2010-09-13 · “image...
Post on 10-Mar-2020
2 Views
Preview:
TRANSCRIPT
“Image acquisition and manipulation protocols for CT-PET
fusion to improve the accuracy of gross tumour volume
localisation for 3D conformal radiotherapy for lung cancer.”
Catriona Hargrave BAppSc: Medical Radiations Technology - Radiotherapy, QUT
Faculty of Science and Technology
Queensland University of Technology
Thesis submitted in fulfilment of the requirements for
the degree of Master of Applied Science (Research)
2010
i
Key words
Image registration, image fusion, tumour volume delineation, clinical protocols,
computed tomography, CT, positron emission tomography, PET, tumour motion, 3-
dimensional conformal radiation therapy, treatment planning, lung cancer.
ii
Abstract
Aims: To develop clinical protocols for acquiring PET images, performing CT-PET
registration and tumour volume definition based on the PET image data, for
radiotherapy for lung cancer patients and then to test these protocols with respect to
levels of accuracy and reproducibility.
Method: A phantom-based quality assurance study of the processes associated with
using registered CT and PET scans for tumour volume definition was conducted to:
(1) investigate image acquisition and manipulation techniques for registering and
contouring CT and PET images in a radiotherapy treatment planning system, and (2)
determine technology-based errors in the registration and contouring processes. The
outcomes of the phantom image based quality assurance study were used to
determine clinical protocols. Protocols were developed for (1) acquiring patient PET
image data for incorporation into the 3DCRT process, particularly for ensuring that
the patient is positioned in their treatment position; (2) CT-PET image registration
techniques and (3) GTV definition using the PET image data. The developed clinical
protocols were tested using retrospective clinical trials to assess levels of inter-user
variability which may be attributed to the use of these protocols. A Siemens
Somatom Open Sensation 20 slice CT scanner and a Philips Allegro stand-alone PET
scanner were used to acquire the images for this research. The Philips Pinnacle3
Results: Both the attenuation-corrected and transmission images obtained from
standard whole-body PET staging clinical scanning protocols were acquired and
imported into the treatment planning system for the phantom-based quality assurance
study. Protocols for manipulating the PET images in the treatment planning system,
particularly for quantifying uptake in volumes of interest and window levels for
accurate geometric visualisation were determined. The automatic registration
algorithms were found to have sub-voxel levels of accuracy, with transmission scan-
based CT-PET registration more accurate than emission scan-based registration of
the phantom images. Respiration induced image artifacts were not found to
influence registration accuracy while inadequate pre-registration over-lap of the CT
and PET images was found to result in large registration errors. A threshold value
treatment planning system was used to perform the image registration and contouring
of the CT and PET images.
iii
based on a percentage of the maximum uptake within a volume of interest was found
to accurately contour the different features of the phantom despite the lower spatial
resolution of the PET images. Appropriate selection of the threshold value is
dependant on target-to-background ratios and the presence of respiratory motion.
The results from the phantom-based study were used to design, implement and test
clinical CT-PET fusion protocols. The patient PET image acquisition protocols
enabled patients to be successfully identified and positioned in their radiotherapy
treatment position during the acquisition of their whole-body PET staging scan.
While automatic registration techniques were found to reduce inter-user variation
compared to manual techniques, there was no significant difference in the
registration outcomes for transmission or emission scan-based registration of the
patient images, using the protocol. Tumour volumes contoured on registered patient
CT-PET images using the tested threshold values and viewing windows determined
from the phantom study, demonstrated less inter-user variation for the primary
tumour volume contours than those contoured using only the patient’s planning CT
scans.
Conclusions: The developed clinical protocols allow a patient’s whole-body PET
staging scan to be incorporated, manipulated and quantified in the treatment planning
process to improve the accuracy of gross tumour volume localisation in 3D
conformal radiotherapy for lung cancer. Image registration protocols which factor in
potential software-based errors combined with adequate user training are
recommended to increase the accuracy and reproducibility of registration outcomes.
A semi-automated adaptive threshold contouring technique incorporating a PET
windowing protocol, accurately defines the geometric edge of a tumour volume
using PET image data from a stand alone PET scanner, including 4D target volumes.
iv
Table of contents
1 Background ......................................................................................................... 1
1.1 Introduction ................................................................................................ 1
1.2 Literature review and background information ..................................... 5
1.2.1 Guidelines for tumour volume definition in radiotherapy ................... 5
1.2.2 Over view of the 3D conformal radiotherapy process ......................... 6
1.2.3 Lung cancer patients referred for 3D conformal radiotherapy ............. 8
1.2.4 Limitations of CT in GTV localisation for lung cancers ..................... 8
1.2.5 The sensitivity and specificity of PET versus CT for imaging lung
cancer ................................................................................................... 9
1.2.6 The rationale of image fusion in treatment planning ........................... 9
1.2.7 The potential of CT/PET fusion to improve GTV definition accuracy
for lung cancers .................................................................................. 13
1.2.8 Potential errors associated with incorporating registered CT/PET
images into the GTV definition process for lung cancers .................. 13
1.2.9 Planning CT and 18 15F-FDG PET image acquisition and quality .........
1.2.9.1 Planning CT image acquisition ...................................................... 15
1.2.9.2 PET image acquisition and reconstruction ..................................... 18
1.2.9.3 PET image quantification ............................................................... 21
1.2.9.4 The effects of motion on CT and PET image quality .................... 22
1.2.9.5 The implications of clinical PET scan access for patient positioning
........................................................................................................ 24
1.2.10 Image viewing and interpretation ....................................................... 25
1.2.11 Image registration techniques ............................................................. 28
1.2.11.1 Manual registration .................................................................... 29
1.2.11.2 Semi-automatic registration techniques ..................................... 29
1.2.11.3 Automatic registration techniques .............................................. 32
1.2.11.4 Validation of image registration ................................................. 38
1.2.12 Contouring techniques ........................................................................ 38
1.2.12.1 Contouring techniques in a treatment planning system ............. 38
1.2.12.2 Reported methods of GTV contouring on PET images ............. 40
1.3 Aims of the project ................................................................................... 42
v
1.4 Ethical considerations .............................................................................. 43
1.5 Research agreement ................................................................................. 46
2 Image acquisition and analysis ....................................................................... 47
2.1 Phantom design and construction .......................................................... 47
2.1.1 Aims of phantom construction ........................................................... 47
2.1.2 Phantom dimensions and functions .................................................... 48
2.1.2.1 Main tank design ............................................................................ 48
2.1.2.2 Moving sphere design .................................................................... 51
2.2 Phantom image acquisition and analysis ............................................... 53
2.2.1 Aims ................................................................................................... 53
2.2.2 Methodology ...................................................................................... 54
2.2.2.1 Phantom CT scan acquisition ......................................................... 54
2.2.2.2 Phantom PET scan acquisition ....................................................... 61
2.2.2.3 Image quantification using the treatment planning system tools ... 65
2.2.2.4 Determination of window width and level for PET images ........... 69
2.2.2.5 Evaluation of the moving sphere on the CT and PET images ....... 72
2.2.3 Data analysis ...................................................................................... 73
2.2.4 Results ................................................................................................ 76
2.2.4.1 Phantom CT scan acquisition ......................................................... 76
2.2.4.2 Phantom PET scan acquisition ....................................................... 79
2.2.4.3 Image quantification using the treatment planning system tools ... 84
2.2.4.4 Determination of window widths and levels for PET images ....... 90
2.2.4.5 Evaluation of the imaged moving sphere on the CT and PETimages
........................................................................................................ 92
2.2.5 Discussion and conclusions ............................................................... 97
2.3 Patient image acquisition and analysis ................................................. 103
2.3.1 Aims ................................................................................................. 103
2.3.2 Methodology .................................................................................... 104
2.3.2.1 Estimation of patient image numbers for protocol trials .............. 104
2.3.2.2 Clinical protocols for acquiring patient PET scans ...................... 104
2.3.2.3 PET scan acquisition .................................................................... 105
2.3.2.4 CT scan acquisition ...................................................................... 106
2.3.2.5 Image analysis .............................................................................. 108
2.3.2.6 Application of phantom-based image windowing results ............ 109
vi
2.3.3 Results .............................................................................................. 110
2.3.3.1 Patient identification and PET scan acquisition protocols ........... 110
2.3.3.2 Image analysis .............................................................................. 112
2.3.3.3 Application of phantom-based image windowing results ............ 112
2.3.4 Discussion and conclusions .............................................................. 116
3 Image registration technique evaluation and development of a protocol for
the clinical trials ............................................................................................. 120
3.1 Phantom based image registration technique evaluation ................... 120
3.1.1 Aims ................................................................................................. 120
3.1.2 Methodology .................................................................................... 121
3.1.2.1 Algorithm tests ............................................................................. 121
3.1.2.2 Baseline registrations ................................................................... 123
3.1.2.3 Auto-registration of the planning CT and PET AC scans using the
MI algorithm ................................................................................ 125
3.1.2.4 Automated PET transmission scan-based registration with the
planning CT scan using the MI algorithm .................................... 125
3.1.2.5 Fiducial marker tests .................................................................... 125
3.1.3 Data analysis .................................................................................... 127
3.1.3.1 Cross correlation and mutual information algorithm tests ........... 127
3.1.3.2 Comparison of auto-registration results of the phantom CT and PET
images ........................................................................................... 127
3.1.3.3 Fiducial marker tests .................................................................... 130
3.1.4 Results .............................................................................................. 131
3.1.4.1 Algorithm tests ............................................................................. 131
3.1.4.2 Comparison of the level of accuracy and reproducibility of AC
scan-based registration versus transmission scan-based-registration
...................................................................................................... 133
3.1.4.3 The effects of motion on the level of accuracy and reproducibility
of automated registration techniques ............................................ 137
3.1.4.4 Fiducial marker tests .................................................................... 144
3.1.5 Discussion and conclusions .............................................................. 145
3.2 Clinical trial of the image registration protocol .................................. 149
3.2.1 Aims ................................................................................................. 149
3.2.2 Methodology .................................................................................... 150
vii
3.2.2.1 Pilot study and RT training .......................................................... 150
3.2.2.2 RT image registration trials using patient data ............................ 152
3.2.3 Data analysis .................................................................................... 154
3.2.4 Results .............................................................................................. 156
3.2.4.1 Pilot study and RT training .......................................................... 156
3.2.4.2 RT image registration trials using patient data ............................ 157
3.2.5 Discussion and conclusions ............................................................. 166
4 GTV delineation technique evaluation and protocol development for the
clinical trial ..................................................................................................... 168
4.1 Phantom contouring tests ...................................................................... 168
4.1.1 Aims ................................................................................................. 168
4.1.2 Methodology .................................................................................... 169
4.1.2.1 Threshold value contouring of phantom PET AC data ................ 169
4.1.2.2 Verification of the geometrical accuracy of the threshold values 170
4.1.3 Data analysis .................................................................................... 173
4.1.4 Results .............................................................................................. 174
4.1.4.1 Threshold value contouring of phantom PET AC data ................ 174
4.1.5 Discussion and conclusions ............................................................. 178
4.2 Clinical trial of the GTV delineation protocol ..................................... 181
4.2.1 Aims ................................................................................................. 181
4.2.2 Methodology .................................................................................... 182
4.2.2.1 Pilot study and RO training .......................................................... 182
4.2.2.2 RO contouring trial using patient data ......................................... 184
4.2.3 Data analysis .................................................................................... 186
4.2.4 Results .............................................................................................. 187
4.2.4.1 Pilot study and RO training .......................................................... 187
4.2.4.2 RO contouring trial using patient data ......................................... 188
4.2.5 Discussion and conclusions ............................................................. 196
5 Conclusions and recommendations .............................................................. 199
Appendix 1: Phantom PET scan pixel and SUV graphs .................................... 200
Appendix 2: Graphs of the registration algorithm test results .......................... 204
Appendix 3: Graphs of the phantom registration results .................................. 209
Appendix 4: RT instructions for the patient image registration trials ............. 214
Appendix 5: Graphs of the results for the RT registration trials ...................... 217
viii
Appendix 6: RO instructions for the patient GTV definition trials .................. 220
6 Bibliography ................................................................................................... 230
ix
List of figures
Figure 1-1 The 3D conformal radiotherapy treatment process ................................... 7
Figure 1-2 2D and 3D images in a treatment planning system reconstructed from the
planning CT ........................................................................................................ 10
Figure 1-3 Dose distribution calculated using a planning CT scan for a lung cancer
patient ................................................................................................................. 11
Figure 1-4 Image fusion software visualisation tools ............................................... 12
Figure 1-5 Partial volume effects caused by thick CT slices .................................... 17
Figure 1-6 Comparison of a patient’s non-attenuation corrected and an attenuation-
corrected emission scan ..................................................................................... 20
Figure 1-7 Image characteristics of PET emission, transmission and attenuation-
corrected emission scans .................................................................................... 21
Figure 1-8 Motion artifacts on a free-breathing planning CT scan of the chest ....... 23
Figure 1-9 Lung and mediastinal CT viewing windows for the chest region ........... 26
Figure 1-10 Effect of different window levels on apparent dimensions of a tumour 28
Figure 1-11 The direction of the translation and rotations of the secondary image
about the image axes relative to the patient ....................................................... 29
Figure 1-12 Example of multiple solutions for contour-based registration .............. 30
Figure 1-13 The potential error in fiducial marker localisation as a factor of slice
thickness and marker size ................................................................................... 31
Figure 1-14 Semi-automated threshold contouring techniques ................................. 39
Figure 2-1 Main tank design: INF / Feet view .......................................................... 48
Figure 2-2 Main tank design: Side view at the mid-tank sagittal plane .................... 50
Figure 2-3 Photograph of moving sphere, including mount and motor .................... 52
Figure 2-4 Photograph of phantom set up for scanning ............................................ 54
Figure 2-5 Moving sphere scanning position in main tank: INF / Feet view ............ 55
Figure 2-6 Central sphere sup/inf scanning position in main tank: side view at the
mid-tank sagittal plane ....................................................................................... 56
Figure 2-7 MM3003 multimodality fiducial markers ............................................... 57
Figure 2-8 Position of fiducial markers on phantom ................................................ 57
Figure 2-9 Scanning positions of the sphere to simulate a 4D CT volume of the
moving lesion ..................................................................................................... 60
x
Figure 2-10 Fiducial marker injection technique ...................................................... 62
Figure 2-11 Contouring of the central rods on scan 3 from the “static” CT scan series
of the phantom .................................................................................................... 66
Figure 2-12 Creation of the 4D models of the moving sphere .................................. 67
Figure 2-13 3D models positioned and converted to contours on the PET AC images
of the phantom .................................................................................................... 68
Figure 2-14 PET AC image pixel data available using the CT/dose tool ................. 69
Figure 2-15 Visualisation of the methods used for determining appropriate window
width and levels for viewing PET images .......................................................... 71
Figure 2-16 Determining appropriate viewing windows for the moving sphere on the
PET images using the 4D model as a template .................................................. 72
Figure 2-17 Images of the series of static CT scans of the phantom ......................... 76
Figure 2-18 Images of series 1 and 2 of the “free-breathing” CT scans of the
phantom .............................................................................................................. 77
Figure 2-19 Images of series 3 and 4 of the “free-breathing” CT scans of the
phantom .............................................................................................................. 78
Figure 2-20 The first test PET scan ........................................................................... 79
Figure 2-21 Test PET scans with 0.0197 MBq/ml concentration of 18-FDG .......... 80
Figure 2-22 Appearance of the fiducial markers on the PET AC images ................. 80
Figure 2-23 AC emission scan images from the different phantom PET scan series 82
Figure 2-24 Transmission scan images from the different phantom PET scan series
............................................................................................................................ 83
Figure 2-25 Plots of the background-activity ratios of the main tank pixel data to the
sphere and central rod pixel data for the PET AC images: Series 5-8 with
conditions as per Table 2-4. ............................................................................... 89
Figure 2-26 Frequency plots of window widths and levels for viewing different
features on the PET transmission scans of the phantom .................................... 90
Figure 2-27 Frequency plots of window widths and levels for viewing different
features on the PET AC scans of the phantom ................................................... 91
Figure 2-28 Overlaid 3D contours of the moving sphere imaged for each scan from
the different free breathing series of CT scans ................................................... 92
Figure 2-29 Graphical representation of the variation in the imaged superior and
inferior aspect of the moving sphere from the free breathing CT scans ............ 94
xi
Figure 2-30 Visual comparison of moving sphere imaged on PET AC scans with the
4D model: Series 4 and 6. .................................................................................. 95
Figure 2-31 Visual comparison of moving sphere imaged on PET AC scans with the
4D model: Series 7 and 8 ................................................................................... 96
Figure 2-32 Method for obtaining image data from the PET AC patient images ... 108
Figure 2-33 PET image viewing window results: Patients 1 - 3 ............................. 113
Figure 2-34 PET image viewing window results: Patients 4 – 5 ............................ 114
Figure 2-35 PET image viewing window results: Patients 7 – 9 ............................ 115
Figure 3-1 Box plots for the registration results for the series 2 PET images of the
phantom ............................................................................................................ 134
Figure 3-2 The standard deviation plotted against the mean for the post registration
parameters: combined AC and transmission scan-based registrations ............ 135
Figure 3-3 Combined registration results for the CT and PET images with all
components of the phantom static .................................................................... 138
Figure 3-4 Combined registration results for the CT and PET images with the sphere
moving ............................................................................................................. 139
Figure 3-5 The standard deviation plotted against the mean for the post registration
parameters – all components of phantom static ............................................... 141
Figure 3-6 The standard deviation plotted against the mean – with the sphere moving
during scanning ................................................................................................ 141
Figure 3-7 Anatomy-based matching criteria for the baseline registrations of the
patient CT and PET data sets ........................................................................... 151
Figure 3-8 Patient positioning and misregistration issues noted during the pilot study
.......................................................................................................................... 156
Figure 3-9 Combined RT results of the patient CT and PET images by registration
technique .......................................................................................................... 158
Figure 3-10 The standard deviation plotted against the mean for the post registration
parameters: automated and manual RT registration results ............................. 159
Figure 3-11 The standard deviation plotted against the mean for the post registration
parameters: AC scan and transmission scan-based automated RT registration
results ............................................................................................................... 160
Figure 3-12 The standard deviation plotted against the mean for the post registration
parameters: manual RT results based on the order the registrations were
performed ......................................................................................................... 163
xii
Figure 3-13 The standard deviation plotted against the mean for the post registration
parameters: automated RT results based on the order the registrations were
performed ......................................................................................................... 164
Figure 4-1 Creation of the 20% threshold contour for the 0.5 cm rod using the semi-
automated contouring technique ...................................................................... 170
Figure 4-2 Verification of the geometrical accuracy of the threshold values ......... 172
Figure 4-3 Threshold values plotted against the volume of interest (central rod)
diameter ............................................................................................................ 175
Figure 4-4 A profile of the pixel values taken through the identified GTV on a PET
AC image .......................................................................................................... 184
Figure 4-5 RO contouring results: Patients 2 – 4 .................................................... 189
Figure 4-6 RO contouring results: Patients 5 – 7 .................................................... 190
Figure 4-7 RO contouring results: Patients 8 and 9 ................................................ 191
Figure 4-8 The percentage differences for the combined RO contours: Patients 2 – 4
.......................................................................................................................... 193
Figure 4-9 The percentage differences for the combined RO contours: Patients 5 – 7
.......................................................................................................................... 194
Figure 4-10 The percentage differences for the combined RO contours: Patients 8
and 9 ................................................................................................................. 195
xiii
List of tables
Table 1-1 Medicare funded criteria and codes for whole body PET for lung cancer
(November 2004)72 25 ............................................................................................
Table 1-2 Threshold methods for contouring a GTV using the PET image data ...... 40
Table 2-1 Capacity (ml) of different compartments in phantom ............................... 51
Table 2-2 The “static” phantom CT scan acquisition parameters ............................. 59
Table 2-3 Phantom conditions for the “free breathing” phantom CT scans.............. 59
Table 2-4 Different PET scanning conditions of the phantom for images to be used
to test image registration and GTV delineation protocols ................................. 64
Table 2-5 Volumes of the components of the phantom on CT – calculated and TPS
generated volumes using different contouring methods .................................... 84
Table 2-6 Volume of the 4D moving sphere on CT – calculated and TPS generated
volumes using different contouring methods ..................................................... 85
Table 2-7 Intra-series mean volumes and the overall mean volumes of the 3D model-
generated contours of the phantom components on the PET AC images .......... 86
Table 2-8 Percentage difference of the 3D model-generated volumes for both the CT
and PET images to the calculated volumes of the phantom components .......... 86
Table 2-9 Average ratios of the maximum pixel value in each rod or the sphere to
the 3.0 cm rod maximum pixel value ................................................................. 87
Table 2-10 Imaged volumes of the moving spherical lesion from the different free
breathing series of CT scans .............................................................................. 93
Table 2-11 Patients whose PET images were not used in the clinical trials ........... 110
Table 2-12 Summary of the patient clinical data whose images were acquired for the
image registration and GTV definition trials ................................................... 111
Table 2-13 Patient GTV, Liver and Lung VOI data ................................................ 112
Table 3-1 Pre-registration offsets of secondary image from the primary for algorithm
reproducibility tests .......................................................................................... 121
Table 3-2 Translation only pre-registration offsets of secondary image from the
primary for algorithm accuracy tests ................................................................ 122
Table 3-3 Translation and rotation pre-registration offsets of secondary image from
the primary for algorithm accuracy tests .......................................................... 123
Table 3-4 Registered CT and PET AC scans of the phantom ................................ 124
xiv
Table 3-5 Summary of the results of the CC and MI algorithm tests ...................... 132
Table 3-6 Repeatability coefficients for the different image-based automated
registration techniques ...................................................................................... 136
Table 3-7 Paired t-test comparisons of AC and transmission scan-based mean
registration results of the phantom CT and PET images .................................. 137
Table 3-8 Repeatability coefficients for the static phantom or moving sphere images
.......................................................................................................................... 142
Table 3-9 T-test comparisons of static or moving sphere for the AC and transmission
scan-based mean registration results of the phantom CT and PET images ..... 143
Table 3-10 The difference in fiducial marker localisation on registered CT and PET
AC images of the phantom ............................................................................... 144
Table 3-11 Repeatability coefficients for the manual and automated RT registration
results ............................................................................................................... 161
Table 3-12 Paired t-test comparisons of the means of the registration parameters for
the RT manual and automated registrations ..................................................... 161
Table 3-13 t-test comparisons of the means of the registration parameters for the RT
AC and transmission scan-based registrations ................................................. 162
Table 3-14 Repeatability coefficients based on the order that the RT performed the
registrations ...................................................................................................... 165
Table 4-1 Threshold values verified for GTV definition by visual match with CT 174
Table 4-2 Mean volumes of the baseline contours of the phantom compared to those
generated using the verified accurate threshold values .................................... 176
Table 4-3 Comparison of the percentage maximum threshold value for the SUV data
based on the contouring results ........................................................................ 177
Table 4-4 Results of the pilot study used to determine the threshold values for
contouring the GTV on the patient PET AC images ........................................ 187
Table 4-5 The mean volumes of the RO contours .................................................. 192
Table 4-6 The percentage differences in the volumes of the RO contours ............ 192
xv
List of abbreviations
3DCRT 3 dimensional conformal
radiation therapy 18F-FDG 18
Flourine-2-flouro-2-deoxy-D-glucose
68Ge 68
Germanium
137Cs 137
Cesium
AC attenuation- corrected AEC automatic exposure
control AMPR adaptive multiplanar
reconstruction BEV beam’s eye view Bq Becquerel CC cross correlation CT computed tomography CTV clinical tumour volume DICOM Digital Imaging and
Communication in Medicine
DRR digitally reconstructed
radiograph FOV field of view GTV gross tumour volume ICRU International Commission
on Radiation Units and Measurements
IM internal margin MBS model based segmentation
MI mutual information MPR multi-planar
reconstruction MRI magnetic resonance
imaging MSCT multi-slice computed
tomography NSCLC non-small cell lung cancer PET positron emission
tomography PTV planning tumour volume QLD Queensland RAMLA row-action maximisation-
likelihood algorithm RO radiation oncologist ROI region of interest RT radiation therapist SCLC small cell lung cancer SM set-up margin SUV standardised uptake value TBR target-to-background ratio TPS treatment planning system VOI volume of interest WW window width WL window level
xv
Statement of original authorship
“The work contained in this thesis has not been previously submitted to meet
requirements for an award at this or any other higher education institution. To the
best of my knowledge and belief, the thesis contains no material previously published
or written by another person except where due reference is made.”
Signature
Date
xvi
Acknowledgements
The Mater Radiation Oncology Centre, the Mater Hospital, South Brisbane, QLD
Australia, in conjunction with the Princess Alexandra Hospital, Annerley,
Brisbane. The Mater Radiation Oncology Centre was the principle clinical
collaborator for this research project. Access was provided to the Pinnacle
Research collaboration and support
3
computer planning system for the conduct of the RO and RT trials as well as the
planning CT scanner for scanning of the phantom. The Mater also supplied funding
for the purchase of the multi-modality fiducial markers used on the phantom images.
Many thanks to the radiation oncologists and radiation therapists who participated in
the image registration and GTV delineation protocol trials, (despite the time it took!).
Nuclear Medicine Department, the Wesley Hospital, Auchenflower, Brisbane. The
Nuclear Medicine Department provided scanning time and technical support for their
PET scanner and donated the radiopharmaceutical 18
F-FDG for the phantom
scanning. They also facilitated the PET scanning and data format requirements for
the patients whose images were used in this project.
Insight Oceania. Insight Oceania donated the use of the Syntegra software and the
import licences on the Pinnacle3
treatment planning system at the Mater Centre and
QUT for the conduct of this research.
I would particularly like to acknowledge the assistance and contributions of the
following people:
Personal acknowledgments
Dr Andrew Fielding: Principal supervisor
Senior Lecturer, School of Physical and Chemical Sciences, QUT
Thanks for your insights, patience and encouragement in bringing the different
aspects of this research together, and most importantly, for taking on the role of
supervisor in the last stages.
xvii
Associate Professor Dr Michael Poulsen: Associate supervisor
Director of the Mater Radiation Oncology Centre and Radiation Oncology QLD (St
Andrew’s Toowoomba)
The clinical aspect of this research would not have been possible without your
enthusiastic support as well as your guidance in focusing on the key clinical issues.
Robyn Guidi
Senior Radiation Therapist – Planning, Mater Radiation Oncology Centre
For assisting with the patient image registration pilot study and for facilitating the
use of the treatment planning system for the clinical trials. Your continued support
over the long period of time it took to complete the research will always be
appreciated.
Prue Raward and Mick Broun
Nuclear Medicine Technologists, Nuclear Medicine Department, Wesley Hospital
Thank you for your assistance with PET scanning the phantom (often at the end of a
long working day). Your support and ideas were invaluable in implementing the
clinical scanning protocols for radiation therapy patients.
Wendy Schumer
Radiation therapist and Applications Specialist, Insight Oceania
Thank you so much for your support, especially with the software and finding
solutions that enabled me to get this project under way. Your interest and
enthusiasm for research always results in great discussions.
Special acknowledgment to Chad Hargrave
I would not have submitted this thesis without your encouragement and support.
xviii
xix
1
1
1.1 Introduction
Background
This project is essentially concerned with identifying and attempting to reduce errors,
using clinical protocols, which may be introduced when two different types of
medical images, computed tomography (CT) and positron emission tomography
(PET) are combined in the process of lung tumour definition in 3D conformal
radiotherapy (3DCRT). This process involves multiple steps starting with the
overlaying of the PET image onto a CT image in a radiotherapy treatment planning
system (TPS) and using the data from both images to define a volume on the images
which, as close as possible, equates to the extent of the cancer within the patient.
Radiotherapy aims to deliver a prescribed amount of radiation to a cancerous tumour
within a patient, requiring multiple daily treatment sessions often extending over a
period of many weeks. 3DCRT uses specific processes that allow a pre-determined
3D radiation dose distribution that conforms as tightly as possible to an individual
patient’s tumour to be precisely delivered while sparing as much healthy (or normal)
tissue from irradiation as possible. However, prior to any radiotherapy treatment
commencing, defining the dimensions and location of a tumour accurately within a
patient is essential and this is a significant component of the pre-treatment (or
treatment planning) process.
3D reconstructions of the internal and external anatomy of a patient using medical
images are an effective, non-invasive method of tumour localisation and definition.
An integral part of the 3DCRT process involves locating and defining tumours using
volumetric CT images. However, the sensitivity and specificity of CT scans in
imaging some malignant pathologies is one of the factors contributing to the
uncertainty of tumour volume definition in the treatment planning process using this
imaging modality.
Image registration and fusion software allows other imaging modalities with
improved sensitivity and specificity for certain tumours (compared to CT) to be
incorporated into the process of tumour volume definition. Image registration refers
2
to the process of translating and rotating one image to align with the anatomy on
another image. Image fusion refers to the process of visualising the registered
images1. Multi-modality image registration was implemented in most clinical sites
using magnetic resonance imaging (MRI) and CT images, most commonly for
enhancing the detection of brain tumours. This enabled the true extent of brain
tumours to be accurately visualised using the superior soft tissue contrast of MRI2, 3
.
The contrast between tissues of similar density such as those in the brain is superior
on MRI compared to CT. Verification of the correct alignment of registered MRI
and CT images is assisted by the fact that both CT and MRI produce high resolution
anatomical images.
PET is a nuclear medicine imaging technique which produces a 3D functional image
using various radiopharmaceuticals to assess different physiological functions of a
patient. Tumours with a high glucose metabolism can be detected using a glucose
compound labelled with radioactive fluorine, 18Flourine-2-flouro-2-deoxy-D-glucose
(18F-FDG). 18F-FDG PET images have demonstrated a higher sensitivity and
specificity for imaging certain primary tumours and any associated involved lymph
nodes compared to CT4
.
The use of registered CT and PET images to define tumour volumes during treatment
planning was hotly debated at the time this research project was commenced in late
2002. There was a lack of familiarity using and interpreting PET images in many
local radiotherapy departments at the time. Paulino and Johnstone’s article titled
“FDG-PET in radiotherapy treatment planning: Pandora’s box?”5
, is indicative that
the debate was still going strong in 2004. Published data was limited in answering
the specific issues relative to the incorporation of PET images into the radiotherapy
treatment planning process.
Generally speaking and particularly to the unfamiliar eye, PET images are almost
featureless backgrounds with some regions of intense uptake, and very little
anatomical information, making the prospect of accurate image registration with CT
difficult to achieve. There are various PET image formats, but initially there was
very limited data to suggest which was most appropriate to use in the registration
process. The issues involved in using CT/PET fusion in radiotherapy were
3
compounded by the introduction of automated image registration software. Up until
this point, all image registration in treatment planning had been performed manually
by the radiation therapist and the ability of the new automated software to accurately
register image data sets was still being proven. There were strong opinions that the
registered PET images should only be used to assist in the visual localisation of
tumours and involved lymph nodes and not for directly creating tumour volumes
based on their pixel data, due to image spatial resolution and issues surrounding
quantification of 18F-FDG uptake levels6
.
The use of CT/PET registration for tumour volume delineation for lung cancer was
chosen for this study as it was anticipated that investigations using image data of the
chest would incorporate many of the issues relevant to the use of PET in
radiotherapy treatment planning. At the start of this project there was a growing
body of published research which provided evidence that CT scans through the chest
and abdomen did not accurately image tumour volumes if they did not take into
account the respiration cycle of a patient7
. There were few studies on the effects of
respiration on PET imaging.
This research project did not seek to address the diagnostic accuracy of 18F-FDG
PET imaging. At the time questions existed and to some extent still exist regarding
the diagnostic accuracy of this type of imaging for differentiating between some
benign (such as inflammation or infection) and malignant pathologies8. During the
patient data acquisition phase of this project it was found that other diagnostic tests
are used to complement 18
F-FDG PET imaging findings when a diagnosis of lung
cancer is made in the clinical setting.
In the first stages of the project, considerable time was spent liaising with various
departments located in different hospitals to develop appropriate protocols for the
acquisition of the PET images that met patient positioning requirements for
radiotherapy treatment planning. Automated image registration software in
treatment planning systems was not widely available and had to be accessed. A
phantom which could be CT and PET scanned, with a component that could simulate
patient respiration, had to be designed and custom built. This first stage of the study
extended over a period of approximately two years. Limited access to PET scan
4
services (there was only one PET scanner in Queensland (QLD) at the time), along
with the high cost of a scan and limited Medicare funding eligibility impacted on the
time it took to acquire the images required for this research.
Since this research was begun, advances in PET technology have seen the
introduction of combined CT/PET scanners that provide “hardware” registered CT
and PET images. However, combined CT/PET scanning was not available clinically
in QLD until the beginning of 2006, which was after the completion of the data
acquisition phase. 4D or respiratory gated CT in radiotherapy has been developed to
eliminate imaging errors associated with imaging the chest and abdomen. This body
of research is presented in light of the technology clinically available during its
conduct. This said, all of the results presented in this study using a stand alone PET
scanner and pre-gated CT techniques can be applied to these new technologies and
techniques.
5
1.2 Literature review and background information
1.2.1 Guidelines for tumour volume definition in radiotherapy
A prescribed radiation dose for a given tumour is based on biological responses of
cancer cells to daily irradiation (or fractions) throughout a course of radiotherapy9
.
Failure to adequately treat a tumour has direct consequences on the ability to achieve
tumour regression and a cure. Tumour volume definition is the starting point for all
the procedures involved in the delivery of radiation therapy as it is the defined
tumour volume that is used to directly design the radiation treatment fields that are
used to deliver the prescribed radiation dose.
There are internationally recognised guidelines developed by the International
Commission on Radiation Units and Measurements10 11
• Gross Tumour Volume (GTV)
for tumour volume definition
in radiotherapy. These definitions provide guidelines that take into account various
patient and technology specific parameters that may impact on the accuracy of daily
coverage of a tumour volume by the treatment fields. These definitions (as quoted
from ICRU reports 50 and 62) are:
The GTV is the volume encompassing all visible malignant
disease on imaging studies (this includes both the primary and any
involved loco-regional lymph nodes).
• Clinical Target Volume (CTV)
The CTV is the volume that contains the GTV plus a margin that
includes subclinical microscopic malignant disease surrounding
the GTV.
• The Internal Margin (IM)
The volume that contains the CTV plus a margin to account for all
movements and variations in site, size and shape of the organs and
tissue contained in or adjacent to the CTV.
• Internal Target Volume (ITV)
The volume encompassing the CTV and IM which relates to the
CTV position in relation to internal and external reference points
preferably rigid anatomical features12.
6
• The Set-up Margin (SM)
To account specifically for uncertainties (inaccuracies and lack of
reproducibility) in patient positioning and alignment of the
therapeutic beams during treatment planning and through all
treatment sessions.
• Planning Target Volume (PTV)
Combines the GTV, and CTV, IM and SM margins. The PTV is
used to define the treatment field geometry.
1.2.2 Over view of the 3D conformal radiotherapy process
3DCRT is currently considered the international gold standard treatment technique
for delivery of external beam radiation therapy13. It aims to increase the therapeutic
ratio of radiotherapy (i.e. a cure with minimal side effects). Most normal tissues can
only tolerate radiation doses which are significantly lower than those required to
eliminate a tumour. Radiation tolerance levels for different tissues and organs are
based on the probability of a particular response to the treatment and its level of
severity (i.e radiation induced pneumonitis in normal lung tissue)14. For some
tissues, again using the lungs as an example, both dose and irradiated volume affect
the severity of side effects15. 3DCRT radiotherapy allows higher doses to be
delivered to a tumour whilst reducing dose to surrounding normal tissues by using
treatment fields that are designed to conform tightly to the shape of the PTV16
.
Unnecessary treatment of surrounding normal tissues due to excessive tumour
volume dimensions will increase patient side effects.
All radiotherapy treatments are divided into two main processes, treatment planning
and treatment delivery. The planning and treatment processes required for 3DCRT
are shown in Figure 1-1. Tumour volume definition in 3DCRT treatment planning is
based on a volumetric CT image acquired with the patient in their treatment
position17. Differences in patient positioning between their treatment position and
their scan acquisition position will result in differences in the patient’s tumour
position relative to their anatomy during treatment delivery. This in turns leads to
incorrect dosage to the PTV and the surrounding normal tissues.
7
Figure 1-1 The 3D conformal radiotherapy treatment process
Planning CT scan performed Patient is positioned as required for treatment delivery.
External marks are defined that will be used for accurate localisation of the treatment fields during treatment delivery.
Planning CT image imported into TPS External localising marks located and defined on CT image in TPS.
The tumour and any loco-regional lymph nodes are localised and defined (GTV definition)
Visualisation and contouring tools in TPS are used in this process.
Normal tissues are localised and defined Visualisation and contouring tools in TPS are used in this process.
Number of treatment fields and their geometry is determined A potential treatment field can be projected through a 3D reconstruction of the
patient’s planning CT scan to assess that it delivers the prescribed tumour (PTV) dose but avoids or limits the radiation dose to the normal tissues.
Radiation dose from all treatment fields is calculated A 3D dose distribution is displayed on the CT image of the patient.
Planned treatment is evaluated and approved The dose distribution to the defined tumour and normal tissues is evaluated.
The treatment plan is delivering prescribed radiation dose to the tumour and the normal tissues it is approved for delivery to the patient.
Treatment field geometry is exported to linear accelerator from TPS Treatment field parameters from approved treatment plan are imported at linear
accelerator where treatment will be delivered.
Treatment is delivered for the
number of specified
treatment days
Each day of treatment the
patient is positioned on the linear accelerator
treatment couch in the same position
that was used when their planning CT
was acquired.
The external localisation marks
defined at CT and on-line imaging
technology are used to determine accurate positioning of each
treatment field within the patient relative to
the defined location of the tumour on the planning CT scan.
The geometry of the treatment fields
designed to cover the tumour volume dimensions defined from the planning
CT scan are delivered for each
daily treatment
Tre
atm
ent p
lann
ing
Tre
atm
ent D
eliv
ery
8
The planning CT scan is imported into a radiotherapy treatment planning system
(TPS) by the treatment planning radiation therapist (RT). The radiation oncologist
(RO) uses the image viewing and contouring tools available in the TPS to localise
and define a patient’s GTV on the treatment planning CT image.
1.2.3 Lung cancer patients referred for 3D conformal radiotherapy
Radiotherapy can be used extensively for the treatment of lung cancer as a part of
initial treatment management or to treat progressive or recurrent disease18. The
decision to offer radiotherapy for lung cancer is based on the histology and stage of
the disease, incomplete surgical resection, the level of morbidity that may result and
the performance status of the patient. 3DCRT can be used to treat the following
groups of patients19 20
• SCLC which has responded well to chemotherapy.
:
• Medically unfit or elderly patients with Stage I and II non-small cell lung
cancer (NSCLC).
• Incomplete surgically resected Stage IIA and IIB NSCLC.
• Patients who have a good performance status for Stage III NSCLC.
These patients most often require treatment to primary tumours and locally involved
regional lymph nodes in the chest region21
.
1.2.4 Limitations of CT in GTV localisation for lung cancers
Accurate localisation of primary tumour volumes and loco-regional nodes using CT
images in radiotherapy treatment planning is limited by a number of factors. CT is
an anatomical image that relies upon recognition and discrimination of abnormal
densities and structure shapes to make an accurate diagnosis. Poor tumour definition
can occur if the tumour is the same density as the normal tissues surrounding it.
Lymph node diagnosis on CT relies on significant enlargement of the node (> 1.0 cm
in diameter)22
.
Three studies compared primary tumour and involved loco-regional node definition
(Van de Steene et al23, Giraud et al24, and Senan et al25). Large variations were seen
9
especially in the presence of atelectasis, with pleural reactions and neoplastic
parenchymal infiltrations which were noted to affect the process of disease
localisation. There was also a significant variation in the interpretation of positive
lymph nodes.
1.2.5 The sensitivity and specificity of PET versus CT for imaging
lung cancer
In Gambhir et al’s summary of PET literature4, the range of sensitivity, specificity
and accuracy of PET’s ability to diagnose malignant tumours across all histologies is
84-87%, 88-93% and 87-90% respectively. Coleman22
described the sensitivity,
specificity and accuracy of PET in primary mass diagnosis in lung cancer as ranging
from 82-100%, 75-100% and 79-94% respectively.
In the study by Fritscher-Ravens et al26 comparing CT, PET and endoscopic
ultrasound with or without fine needle aspiration, it was found that the sensitivity,
specificity and accuracy of PET to detect malignant nodes was 73%, 83% and 79%
respectively. CT had sensitivity, specificity and accuracy of 57%, 74% and 67%
respectively demonstrating PET’s superiority in involved node detection.
MacManus et al27
provided evidence that PET staging scans influence the
management of lung cancer patients, particularly impacting on the approach taken
for individual patient radiotherapy treatments.
1.2.6 The rationale of image fusion in treatment planning
Planning CT scans are not only used to visualise the tumour within the patient for
contouring purposes. 2D multi-planar reconstructions (MPRs) or 3D volumetric
reconstructions (see Figure 1-2) are used to virtually project and determine the
geometry of intended treatment fields to cover a given tumour volume via images
reconstructed from the CT scan28, 29. This is known as virtual simulation. Digitally
reconstructed radiographs (DRRs) are used to determine beam’s eye views (BEV’s)
of the geometry of each treatment field and its coverage of the PTV 30. DRRs and
BEVs are also used as reference images during treatment delivery to check the
accuracy of treatment field position within the patient.
10
Figure 1-2 2D and 3D images in a treatment planning system reconstructed from the planning CT (a) A transverse or axial 2D slice, (b) A MPR sagittal 2D slice, (c) A DRR with a BEV of an anterior treatment field conformed to the PTV (yellow), (d) A 3D render of the skin surface of the patient.
(a) (b)
(c) (d)
Human tissues are not uniformly dense and the density of these tissues affects the
absorption of radiation within the patient. There is a direct relationship between CT
numbers and electron densities of tissues. Treatment planning programs convert the
patient’s planning CT scan into tissue specific densities based on the linear
attenuation coefficient and then use this data to perform absorbed dose calculations
within the patient (see Figure 1-3)31. It is important that the planning CT scan is an
accurate representation of tissue heterogeneities to ensure accurate dose calculations.
It is possible to improve CT’s ability to image tumours using contrast media.
However, it is not desirable to use CT scans acquired with contrast in planning CT
scans. The density of the contrast media (which will not be present during the
patient’s treatment) will affect the accuracy of the dose calculations of the planned
treatment with respect to the delivered treatment32.
11
Figure 1-3 Dose distribution calculated using a planning CT scan for a lung cancer patient The PTV (solid yellow contour), the planned treatment field coverage of the PTV and the calculated dose distribution are shown relative to the patient’s planning CT scan. The planned treatment fields are an anterior (red), a left anterior oblique (blue), a left posterior oblique (yellow), and a posterior (green). The dose distribution in the patient is indicated by the isodose lines for different levels of absorbed dose, measured in Gray (Gy).
Radiotherapy treatment planning systems have image registration and fusion
software options. Image registration and fusion allows other imaging modalities
which are more sensitive and specific for imaging the full extent of a patient’s cancer
(a contrast enhanced CT, MRI or PET scan for example) to be incorporated into and
improve the accuracy of the GTV definition process in 3DCRT33, 34. Image fusion
software visualisation tools (see Figure 1-4) enable the unique or complementary
information from the secondary image to be used in conjunction with the planning
CT scan to localise and contour the GTV35
. The planning CT scan is still used as the
primary image data set for treatment field visualisation and localisation, and for dose
calculations.
12
Figure 1-4 Image fusion software visualisation tools (a) From left to right: transverse, sagittal and coronal MPRs of the planning CT scan of a patient with a brain tumour. (b) An MRI scan of the same patient demonstrating the tumour in the right parietal lobe of their brain. A look up table with a different colour scale has been applied to this image. (c) Different displays of the fused images allowing for the better imaged dimensions of the tumour on the MRI to be localised on the planning CT scan. From left to right: checker box display, blended display, and cut-away moving box display.
(a)
(b)
(c)
13
1.2.7 The potential of CT/PET fusion to improve GTV definition
accuracy for lung cancers
It has been reported from clinical trials that GTVs are frequently altered by the ROs
when CT-PET fusion has been performed on lung patients. Erdi et al36 performed a
study on the use of PET-CT fusion to provide enhanced GTV and involved node
definition in the 3D planning of lung cancer patients. They found that for all
patients, the PTV (derived from combining the involved nodes with the GTV)
delineated from CT alone changed when the PET image was registered. PET
reduced the PTVs in two patients due to discernment of the tumour within atelectasis
while for other patients it detected small, involved nodes that would not be
considered malignant with CT alone. The results of a similar study by Mah et al37
reflected the same findings. PET-CT registration was found to identify positive
nodes that were not considered malignant on CT as well as enhancing tumour
definition for poorly defined tumours.
1.2.8 Potential errors associated with incorporating registered
CT/PET images into the GTV definition process for lung
cancers
High levels of accuracy are required throughout all the processes involved in
delivery of radiation therapy. The margins applied to achieve a PTV are required
due to the inherent geometrical uncertainties associated with localising any point in a
patient on any given day. Both patient physiological factors and precision levels of
the technology used in radiotherapy treatment planning and delivery will impact on
these geometrical uncertainties38
.
Van Herk39
• A systematic error is the result of any variation during treatment
preparation which would lead to an initial displacement of the dose
derived a methodology and formula for deriving margins to account for
geometric uncertainties in both the planning and treatment processes. This formula
is based on the standard deviation of systematic and random variations (or errors) in
the delivery of planned treatment fields to a tumour. These two classes of errors can
be defined as:
14
distribution relative to the CTV. A systematic error would be delivered
for all treatments.
• A random error is the result of variation during treatment execution, and
its magnitude will vary for each fraction of the patient’s treatment.39, 40
Based on these definitions any uncertainties or errors that may be introduced when
using registered PET and CT images for contouring a GTV in the 3DCRT process
would be classed as systematic. Therefore the processes involved in acquiring,
registering and contouring of the CT and PET images need to be examined, as any
errors in this process would be continued throughout the entire course of a patient’s
treatment. It is also important to reduce the level of any systematic errors where
possible so that optimal margins can be applied to a CTV. Optimal margins for
radiotherapy of lung cancers are those that ensure adequate CTV coverage despite
uncertainties in GTV localisation while treating the smallest possible healthy lung
volume.
Armstrong41
highlighted multiple factors relating to technology and techniques in the
treatment planning process that could impact on accurate target volume definition for
3DCRT of lung cancer when using CT images alone. Image acquisition techniques,
image quality, accurate patient positioning in their treatment position and tumour
motion will directly impact on the accuracy of using the planning CT scan to target a
tumour for daily radiotherapy treatment. These image acquisition factors also
translate to PET images that will be used in the GTV definition process.
The use of more than one imaging modality through image registration can increase
the geometrical uncertainty associated with contouring using these images42. During
the image registration process it is important to ensure that the same point in the
patient is aligned on the two images so as not to introduce an offset error in
localisation and definition when the tumour volume is defined on the secondary
image. Accurate image registration can be affected by the method used to register
the images and inter-user variability when registration results are assessed43
.
Inter-observer variability between the ROs’ contouring results in GTV definition is
another source of geometrical uncertainty that needs to be considered42. The aim of
15
incorporating PET image data into the 3DCRT treatment planning process is to
reduce inter-user variations due to difficulty in ascribing CT densities to pathology.
Previously mentioned lung cancer GTV definition studies 23-25
• Training and individual experience in image interpretation
also identified the
following factors as affecting contouring accuracy:
• Problems with the contouring methodology in the TPS.
1.2.9 Planning CT and 18
Image quality is important when defining and localising structures for 3DCRT
treatment planning. The contrast and spatial resolution of the images affects the
ability to differentiate between different tissues as distinctly different objects. Partial
volume and patient motion artifacts can also lead to structure misrepresentation and
hence incorrect definition. Image voxel size is an important feature when
considering image quality, particularly partial volume effects.
F-FDG PET image acquisition and quality
1.2.9.1 Planning CT image acquisition
There are many CT scan parameters that, depending on those selected prior to
acquisition, will affect image quality. The intended use of the resulting image will
determine actual scan parameters for each patient’s scan. Desirable qualities for
planning CT scans are:
• The patient must be scanned in their treatment position to avoid tumour and
critical tissue localisation errors.
• The entire patient’s external contours, the couch and any relevant stabilisation
equipment should be imaged. This is essential for calculating dose
absorption within the patient and the linear accelerator monitor units for each
treatment field.
• The volume of the patient imaged should include sufficient anatomy for
accurate treatment field localisation and dose volume calculations.
• High quality axial (transverse) images with high contrast resolution between
tissues and minimal partial voluming artifacts especially on sagittal and
coronal multi-planar reconstructed images.
• High spatial resolution DRR images.
16
• Minimal artifacts from patient movement during scanning.
Radiotherapy departments routinely use multi-slice CT scanners dedicated to
acquiring planning CT scans for 3DCRT treatment planning. These scanners have a
flat couch top to which a solid carbon fibre panel which replicates those that form
parts of the treatment couch on the linear accelerator can be added. A system of
positioning lasers are mounted in the scanner room to assist with localising external
reference marks on the patient that will be used to localise the treatment fields within
the patient. Wide-bore multi-slice CT (MSCT) scanners with diameters up to 85cm
have been developed to accommodate the stabilisation equipment and patient
positions required for radiotherapy17, 44
. The Siemens Somatom Open Sensation,
used for acquiring the CT images for this research, is a 20 slice, wide-bore scanner.
Scanning parameters can be selected by the RT in a similar fashion to diagnostic CT
imaging; however certain planning CT scan imaging parameters are only selected
using pre-defined protocols. Image reconstruction algorithms and filters and kV are
determined during the commissioning phase of the CT scanner when the CT number
to electron density conversion is calibrated45. These parameters are never altered as
this will affect the accuracy of dose calculations within the treatment planning
system. Automatic exposure controls (AECs) are routinely used in planning CT scan
acquisition to reduce patient dose. Using AEC software the mAs is automatically
determined by the scanner from the topogram (z-axis modulation) and a priori
feedback (angular modulation) during the scan acquisition46, 47
.
Fast acquisition times are desirable for planning CT scans to reduce artifacts
introduced by patient movements on the couch top during acquisition. Increasing
pitch can allow larger volumes of the patient to be imaged quickly. However this
can introduce geometrical distortions and result in poor z-axis resolution in the
images48, 49
. A smaller pitch will increase patient radiation exposure. Optimal pitch
for imaging is chosen to give maximum scan speed, image quality and minimal
radiation dose. For planning CT scans, pitch is routinely selected from the default
values in region specific protocols which were determined during the commissioning
stage of the CT scanner.
17
The field of view (FOV) and slice thickness are two scanning parameters that are
regularly modified for individual patient planning CT scans. Wide-bore MSCT
scanners such as the Siemens Somatom Open Sensation not only have a large
aperture to accommodate stabilisation equipment and radiotherapy patient
positioning, but also have extended FOVs to enable complete imaging of the two.
This scanner can use its extended FOV to reconstruct images up to 82 cm in
diameter50
. Pixel size is influenced by the FOV. The smaller the imaged FOV the
smaller the pixel size of the image will be, resulting in higher image spatial
resolution. The smallest possible FOV that will include the entire patient’s external
contours, the couch and any relevant stabilisation equipment is selected for planning
CT scans.
The z-axis of a voxel in a CT image equals the image slice thickness. The slice
thickness used for a particular examination is determined by the size of the object
being imaged. For example, imaging of nodes or small lesions requires 2-3 mm
thick slices to ensure adequate sampling and to minimise partial volume effects
(demonstrated in Figure 1-5), whereas imaging the chest or liver only requires 8 mm
slices51. Slice thickness is also important in CT acquisition to generate DRRs with
high spatial resolution. The spatial resolution of DRRs is primarily limited by the
voxel size of the CT data52
.
Figure 1-5 Partial volume effects caused by thick CT slices The images below are from left to right, a transverse, a sagittal and a coronal slice of a planning CT data set. The arrows on the images indicate the different image axes relative to the patient. Yellow = the x axis (the left/right dimensions of the patient). Green = the y axis (the anterior/posterior dimensions). Red = the z axis (superior/inferior dimensions). When large slice thicknesses are selected (3cm for this scan) partial volume effects are most noticeably seen in the z axis of the image, resulting in poor z axis and DRR spatial resolution
18
1.2.9.2 PET image acquisition and reconstruction
PET images are achieved by labelling a pharmaceutical with positron emitting
radioisotopes; the pharmaceutical is then administered to the patient either
intravenously or by inhalation. Two 511 keV photons emitted at 180 degrees from
each other are the result of the annihilation process of an emitted positron. The
energy of these photons is sufficient to allow most of them to pass through the body
of the patient53
.
A PET scanner consists of a circular array of detectors. Each detector is able to form
a coincidence line with any one of the opposing detectors. A detector can form
multiple coincidence lines with detectors across from it, forming a fan-beam
response, or at any given angle, such that a series of parallel coincidences are
formed. The positional information of the positron emitter is achieved by recording a
large number of coincidence events. The location and direction of any given
coincidence line is unique. This information is stored as 2-D matrices called
sinograms which are used to reconstruct a cross-sectional tomographic image53
.
There are many factors affecting PET image quality and detection sensitivity, most
of which cannot be altered during scan acquisition as they are intrinsic to the
radiopharmaceutical properties, scanner design or the object being imaged. PET
detector design (size and spacing)54, positron range prior to annihilation, and the non-
colinearity effect seen in the two 511keV photons will all affect PET image spatial
resolution55. Detector composition, size, position and number, and the imaged voxel
size, affect overall detection sensitivity. The voxel size of PET images is inherent to
the detector design and the image reconstruction algorithm used. There is reduced
image sensitivity in detecting objects that equal to or smaller than the detector
resolution or smaller than the image voxel size due to count recovery losses56.
Objects equal in diameter to the PET scanner resolution have been shown to have a
recovery coefficient of 31.6%57
. The recovery coefficient is a ratio of the measured
activity in an object in the PET image divided by its true activity.
If an imaged object is partially within a voxel then both axial and transverse partial
voluming effects can be seen. Regular shaped objects (large, circular and widely
spaced) are less likely to suffer partial voluming effects than irregularly shaped
19
objects (small, thin and close together)58. The ratio of activity in a volume of
interest to the activity surrounding it, termed the target-to-background ratio (TBR),
affects the contrast resolution of PET images59. Modifying the scan acquisition times
and injected activity (PET imaging parameters which can be altered) can increase
contrast-to-noise ratios, increasing detection sensitivity59. Clinical scanning times
and activity concentrations are tested and evaluated during PET scanner performance
tests60
.
It is recommended that PET imaging studies for lung cancer diagnosis use a 3D
whole body attenuation-corrected image acquisition protocol. Small lesions and
those deep within the body are more accurately detected using this scanning
protocol22. Whole body PET involves imaging the patient in sections (called
frames), the length of which depends on the dimensions and number of detector
rows. A table moves the patient through the scanner as each frame is acquired. The
image can be acquired in 2D or 3D mode. 2D mode uses inter-planar tungsten septa
between detector rows in the axial (z axis) direction of the scan, limiting coincidence
detection to the transaxial (x and y) directions. 3D mode scans without the septa
allows for coincidence detection from all three planes, which can increase image
sensitivity by a factor of 4-653
.
Non-uniform absorption of some of the photons within the patient may produce
image distortions such as regional non-uniformities, intense objects and edge
effects61. The effects can be caused by the high density of the skull or low density of
the lungs, affecting the absorption of emissions and can be corrected for by
determining the probability of attenuation for all sources along a given line of
response by using the dimensions and densities of a transmission scan (see Figure 1-
6). Small objects are also more accurately imaged with attenuation corrected
images62
. An attenuation corrected (AC) emission scan is the result of applying
attenuation correction probabilities obtained from the transmission scan dimensions
on the stored data of the emission scan.
20
Figure 1-6 Comparison of a patient’s non-attenuation corrected and an attenuation-corrected emission scan (a) The emission scan of a patient without attenuation correction: note the darkness of the image in the mediastinum and the skin flaring. (b)The AC emission scan: note the improved resolution and the change in the size of the tumour.
(a) (b)
The transmission scan can be achieved using either a gamma source (usually 68Ge or 137Cs) or a CT scan depending on the type of scanner configuration55. Stand alone
PET scanners use a rotating single gamma source to acquire the transmission scan
simultaneously with each emission frame. Transmission scans are performed with the
start point above the first emission frame and below the last emission frame.
CT/PET scanners consist of a CT and PET scanner combined with a single couch
assembly. A CT scan is performed as a separate scanning protocol just prior to the
PET emission scan and is then used for applying attenuation correction to the
emission scan once it is completed. The Philips Allegro PET scanner used for
acquiring the PET images for this research is a stand alone PET scanner with a 137Cs
gamma source for acquiring transmission scans60
. Transmission scans acquired with
a gamma source have poor contrast resolution compared to CT/PET scanner
transmission scans acquired using the CT component of the scanner (see Figure 1-7).
21
Figure 1-7 Image characteristics of PET emission, transmission and attenuation-corrected emission scans (a) PET emission scan (b) Gamma source transmission scan (c) CT transmission scan (d) AC emission scan
(a) (b) (c) (d)
1.2.9.3 PET image quantification
A PET scan is essentially an image of emitted annihilation photons, from within the
object, counted by the detectors. PET scans can also be quantified by using a
standardised uptake value (SUV). The SUV in 18F-FDG imaging is used as a tool for
indicating malignancy. At its most basic the SUV is the measured concentration of
FDG in an image normalised to total injected activity and patient weight as
demonstrated in the equation below63
.
t(g)body weigh / (Bq) dose injected(Bq/g)ion concentrat tissue SUV =
The use of body surface area for normalisation has been shown to provide a more
robust SUV value than body weight64. There are other methods of determining SUV
such as kinetic models that require blood samples midway through the scan
acquisition65. There has been much debate over the application of SUVs in
quantifying a PET image due to many sources of variability such as patient size and
body composition, differing post-injection scanning times, patient glucose levels, and
the effects of the dimensions of a lesion on image recovery coefficients and partial
volume effects6
.
22
1.2.9.4 The effects of motion on CT and PET image quality
Patient respiration causes not only the displacement of the diaphragm and the chest
wall but also tumours and lymph nodes. The displacement between the inhale and
exhale position of a tumour is referred to as the tumour’s range of motion66.
Seppenwoolde et al’s67
real-time study measured the 3D range of motion for a group
of 20 patients, tracking implanted gold seed markers using fluoroscopic imaging
during treatment. The range of motion in the superior to inferior direction of the
patients was 12 mm ± 6 mm for tumours in the lower lobes and those not attached to
rigid structures, or 2 mm ± 2 mm for upper lobe tumours or those attached to rigid
structures. The range of tumour motion in the left-to-right and anterior-to-posterior
directions is significantly less (1.2 mm ± 0.9 mm and 2.2 mm ± 1.9 mm
respectively).
The time it takes image the whole chest with a Siemens Somatom Open Sensation
scanner is less than 10 seconds. On average a patient’s breathing cycle (from
inspiration to expiration) is 3 – 4 seconds67, 68, resulting in a respiration rate of 15 to
20 cycles per minute. The imaged dimensions of any structure or tumour under the
influence of respiration are dependent on the position of the volume of interest
relative to the timescale of the acquired images. Without breath hold techniques or
coordination of scan acquisition times with the patient’s respiratory cycle, respiration
induced motion artifacts on CT will occur. These artifacts are typically demonstrated
as incomplete imaging of structures, such as the diaphragm, when a patient breathes
freely during scan acquisition (see Figure 1-8). Tumour volume dimensions are not
accurately imaged and neither is their full range of motion within the patient
captured7
.
23
Figure 1-8 Motion artifacts on a free-breathing planning CT scan of the chest The sagittal (a) and the coronal (b) images of this patient have motion artifacts demonstrated as the irregular imaging of the surface of the diaphragm.
(a) (b)
Data collection and reconstruction techniques for 4D CT and respiratory gated CT
are synchronised with a patient’s respiratory cycle. 4D CT images significantly
increase the ability to accurately image both the dimensions and the full range of
motion of a tumour volume compared to free-breathing scans66. 4D CT results in
multiple image data sets, each representing a phase of the patient’s respiratory
cycle69. A respiratory gated scan images a particular portion of the patient’s
breathing cycle and hence only one image data set is obtained using this method66
.
4D or respiratory gated CT images are registered with a free-breathing planning CT
scan. 4D and respiratory gated CT scanning protocols are becoming more widely
implemented. However it is standard clinical practice to also acquire a free breathing
planning CT scan when using 4D CT images. This allows for tumour volume
definition using the respiration correlated images, while dose calculations are based
on the free-breathing scan.
Respiratory motion artifacts can occur for all imaging modalities66. There have been
investigations into the effects of motion on PET scans. It is felt that due to the
slower scan acquisition times for stand-alone PET scanners that the full positional
range of structures and tumours will be imaged. The recommended acquisition time
per emission frame for the Allegro PET scanner is 3 minutes. Each transmission
frame using the 137Cs takes approximately 2 minutes60. These acquisition times are
many times greater than a patient’s breathing cycle (e.g. approximately 45 – 60
cycles per emission frame) producing a time-averaged image70.
24
1.2.9.5 The implications of clinical PET scan access for patient positioning
Planning CT scans of lung cancer patients are performed with the patient positioned
supine with either their arms up above their head or down by their sides depending
on the location of the GTV. The majority are scanned with their arms up to avoid
irradiation of their arms during treatment. Patients with GTVs in the apex of the
lung will be treated with their arms down for the same reason. All of these patients
will require some form of head and neck support to keep their chin out of the way of
any treatment fields. The range of the CT scan is usually from below their chin to
below the diaphragm to ensure that the entire lung volume is included in the scan.
For whole-body PET scanning, patients are scanned from the level of their external
acoustic meatuses (EAMs) to the mid thigh region. Scan times can range from 20-30
minutes depending on patient height. Therefore a comfortable position in which the
patient is least likely to move is chosen. Typically the patient is positioned supine on
a curved couch top with a mattress, with a pillow under their head with their arms at
their sides. Their arms may be positioned above their head if necessary to fit their
shoulder region through the scanner. The diameters of PET scanner apertures are
significantly smaller than those of CT scanners.
It is essential that if a PET scan is to be used for image registration with the planning
CT that the patient should be in the same position as their treatment position for both
scans. The Erdi et al36 CT/PET fusion study makes specific mention of efforts to
limit tumour localisation errors by ensuring that patients were all imaged in the same
position for PET and CT. Several studies have highlighted that one of the main
problems in accurately registering whole body diagnostic PET images with planning
CT scans is differences in patient positioning. Using a flat couch top insert and
radiotherapy stabilisation equipment for the diagnostic and staging PET scan is
therefore recommended34, 36, 37, 71
.
Access to PET imaging in Australia is restricted by the availability of PET scanners
and the eligibility of patients for Medicare funding. During the image acquisition
phase of this research (2004 – 2005), there was only one PET scanner in QLD. This
scanner was situated off-campus to all but one radiotherapy department in the state.
The Medicare Benefits Schedule is a listing of the Medicare services subsidised by
25
the Australian government. For lung cancer patients, funded PET imaging is limited
to whole-body staging PET scans. Table 1-1 lists the Medicare funded criteria and
codes for whole body PET for lung cancer patients during the patient image data
acquisition phase for this study 72
.
Table 1-1 Medicare funded criteria and codes for whole body PET for lung cancer (November 2004)72
Code Purpose
61523
Whole body FDG PET study, performed for evaluation of a solitary pulmonary nodule where the lesion is considered unsuitable for transthoracic fine needle aspiration biopsy, or for which an attempt at pathological characterisation has failed.
61529 Whole body FDG PET study, performed for the primary staging of proven non-small cell lung cancer, where curative surgery or radiotherapy is planned.
Generally the whole body PET for diagnosis and staging of lung cancer patients is
the only scan available for use in their treatment planning process. As yet there is no
Medicare rebate for a PET imaging performed solely for the purpose of radiotherapy
treatment planning. This has implications for acquiring PET scans for radiation
therapy purposes. If the staging PET scan is performed without consideration of the
patient’s radiotherapy treatment position, the accuracy of registering this image with
the planning CT scan for the purpose of GTV definition is compromised.
1.2.10 Image viewing and interpretation
CT has good contrast and spatial resolution as well as high geometrical accuracy
making visual identification of the boundaries of distinctly different anatomical
features possible. Visualisation of CT images is based on greyscale conversion of
CT numbers. CT numbers are converted to a 256 level grey scale for viewing.
Window widths and levels are used to determine how the grey scale is applied to the
CT numbers73
• Window level (WL) determines the mid-range CT number.
. Window level and width are defined as:
26
• Window width (WW) determines the upper and lower range of CT numbers
to be viewed.
Pixels above the upper value are viewed as white while pixels
below the lower range are display as black.
The range of CT numbers between the upper and lower limit are
displayed in the greyscale range
Appropriate window levels are essential for viewing structures of different densities, as shown in Figure 1-9. Figure 1-9 Lung and mediastinal CT viewing windows for the chest region (a) Lung viewing windows used for visualising tumours in the lung tissue (WL=-300, WW=1300). (b) Mediastinum viewing windows used for visualising involved lymph nodes (WL=800, WW=400)
(a) (b)
For PET image interpretation the key consideration is that 18F-FDG PET images
glucose metabolism (i.e., the uptake of glucose by tissues within the body). There is 18F-FDG uptake in normal tissues due to their glucose metabolism74. It is essential to
understand the expected levels of uptake in the various normal tissues, as this can
assist with differentiating between normal structures, tumours, involved lymph nodes
or metastatic disease in the patient. The expected levels of 18
• High uptake in the brain, ureters, kidneys, bladder, active muscle, heart (fed
state), the left ventricle of the heart in some patients and the bowel.
F-FDG in normal
tissues are:
27
• Moderate uptake in active bone marrow (typically seen in the vertebral bodies
and sternum of adults), the liver and lactating breasts or breast implants.
• Low uptake in the bowel, resting muscles and the heart (fasted state)
PET studies are usually performed with the patient in a fasting state so that expected
normal tissue uptake occurs. Normal tissue or background uptake can vary from one
patient to the next due to individual patient metabolism. Hyperglycaemia and
hypoglycaemia affect the uptake of 18F-FDG throughout the body. Hyperglycaemia
results in less 18F-FDG uptake as the pharmaceutical competes with blood glucose.
Hypoglycaemia results in higher uptake in normal muscle tissue and reduced uptake
in tumours. The effect of either of these conditions is to reduce image contrast,
making it harder to distinguish malignant tumours75. When using 18F-FDG PET
images to locate malignant disease an awareness of high uptake levels relating to
benign pathologies, such as inflammation, is important. Nuclear medicine
radiologist guidance on image interpretation is recommended when PET images are
to be used in radiotherapy treatment planning5
.
Qualitatively, uptake within a lesion can be compared with the uptake of the brain
because as the brain metabolises only glucose it is expected that it will have the
highest possible level of uptake. Tumours have a high glucose metabolic rate so
uptake in the brain can be used a reference by which uptake in tumours can be
compared75, 76. Window levels will be relative to the voxel with the highest
measured 18F-FDG uptake for PET AC images. Therefore the grey scale levels used
for windowing for a PET image typically show the brain as the brightest part of the
image with all other regions scaled back from this level, with black indicating no
uptake. Due to this relative scaling, selecting different window widths and levels
will result in different visual sizes of a volume of interest on a PET image (see
Figure 1-10). Therefore reliable window thresholding needs to be developed for
viewing PET images for contouring GTVs34
.
28
Figure 1-10 Effect of different window levels on apparent dimensions of a tumour The two images below demonstrate the effect of different window levels for PET images on the visual size of a tumour. The PET images have been registered and fused with the corresponding patient CT data.
1.2.11 Image registration techniques
Images are classed as either the primary data set or secondary data sets for the image
registration process. In radiotherapy treatment planning, the planning CT scan is
conventionally defined as the primary data set. Image registration at its most basic
involves the translation and rotation of a secondary data set to align corresponding
patient features with the primary image. The primary image remains fixed, while
there are 6 degrees of freedom (3 translations and 3 rotations about the 3 Cartesian
axes of the image) that can be applied to the secondary data set77. Figure 1-11
demonstrates the direction of the translation and rotations of the secondary image
about the image axes relative to the patient. The transformation of the secondary
image onto the primary image may be performed using different registration
methods, broadly classed as manual, semi-automatic, or automatic techniques1, 78
.
29
Figure 1-11 The direction of the translation and rotations of the secondary image about the image axes relative to the patient The arrows on the images indicate the direction of translation and rotation of image axes relative to the patient. Yellow = the x axis translations and rotations, green = the y axis translation and rotations, and red = the z axis translations
1.2.11.1 Manual registration
In manual image registration, the user interactively translates and rotates the
secondary image until it is aligned with the primary image. This method of
registration relies on image quality and the user to identify and match features on
both data sets. Of all the techniques this method is the most dependent on the skill
and experience of the user in order to obtain accurate registration43
.
1.2.11.2 Semi-automatic registration techniques
Semi-automatic techniques rely on the user to identify surfaces or landmarks on both
image sets. There are two approaches to using this method of registration. Either
contoured anatomical surfaces or anatomical points and markers are used to form the
basis of the registration79. Once the contours or points are defined separately on each
patient image an algorithm is used to automatically register the images based on the
defined contours or the points. Inaccurate definition of features prior to registration
will affect the accuracy of the registration outcomes. Contour or point-based semi-
automatic registration requires the image to have high resolution so that features are
able to be clearly identified by the user43
.
A described method of semi-automatic feature-based registration uses the chamfer
matching algorithm to automatically register the images based on the lung surface
30
contours80. Contour matching algorithms are most likely to fail if natural symmetries
exist in the contours which make multiple registration solutions possible. Figure 1-
12 demonstrates that while registration may appear to be achieved in one plane (the
transverse alignment as shown in Figure 1-12A) true registration may not occur in
other planes. Due to the symmetrical geometry of the feature the algorithm can find
multiple solutions in another plane (Figure 1-12B or C) 81
.
Figure 1-12 Example of multiple solutions for contour-based registration For an object with natural symmetries, correct alignment of two images may be demonstrated in one plane (the transverse view shown in A), but may or may not be correctly aligned in other planes (as shown in B and C)
Point-based registration involves identifying the same point, relative to the patient,
on the two images. As it provides fewer match points it is a more efficient form of
registration but does require a minimum of three non-colinear points to reduce the
possibility of multiple solutions79. The points chosen can be intrinsic (small
anatomical features) or extrinsic, such as fiducial markers. Locating small discrete
anatomical features on a PET image is limited by the low resolution of these
images43
. Fiducial markers can provide a means of overcoming this problem.
Fiducial markers are routinely utilised in radiotherapy for localising set-up reference
marks on the patient’s skin surface on their planning CT scan. CT fiducial markers
are radio-opaque and are typically made from thin wire or a small ball bearing.
These are placed over external localisation marks (such as tattoos) on the patient’s
Transverse plane show
n in A
31
skin at the time of their planning CT scan. Once the patient field geometry has been
finalised in the TPS these fiducial markers are identified on the planning CT scan
and are then used to determine set-up instructions for correct treatment field
positioning within the patient relative to the patient’s external localisation marks17
.
The size of a fiducial marker is important and the size chosen is generally dependant
on the spatial resolution of the scanner and the voxel size of the image. If the
fiducial marker is smaller than the spatial resolution of the scanner then it may be too
small to be seen on the image. Anything larger than the size of a voxel will result in
an increase in partial voluming effects of the fiducial marker. The relationship
between slice thickness and CT fiducial marker size is important to reduce marker
localisation errors on the planning CT scan. The positional error in the z-axis
location of the patient’s tattoo on CT is equal to the slice thickness if the fiducial is
imaged over two consecutive slices. If it is imaged over three slices the error is up to
twice the slice thickness (see Figure 1-13).
Figure 1-13 The potential error in fiducial marker localisation as a factor of slice thickness and marker size The blue fiducial marker in (a) is just smaller than the slice thickness and will only be imaged on slice 2. The orange marker in (a) is the same as the blue marker but its centre is directly over where the edges of slices 4 and 5 meet, therefore it will be imaged on both slice 4 and 5. The blue fiducial marker in (b) is slightly larger than the slice thickness and while it is centred over slice 2 it will be imaged over slices 1-3. The orange marker in (b) is the same size as the blue marker but will only be imaged over slices 5 and 6.
(a) (b)
32
A technique called “zeroing the couch” when CT scanning can assist in reducing the
positional localisation of a fiducial marker as a result of partial voluming effects.
This technique involves aligning lasers with the centre of the fiducial marker
visually. The longitudinal or Z value of the CT couch is re-calibrated to 0 where the
laser coincides with the couch. The z value of all the other CT slices in the scan will
be relative to this “zeroed” slice.
Studies utilising fiducial markers for point-based PET-CT registration have used
identical markers during the acquisition of both the PET and CT scans, requiring the
marker to be both radio-opaque and PET avid82. Therefore the fiducial markers need
to be able to contain the PET radiopharmaceutical and also an optimal size so that
they can be imaged by the PET scanner (i.e. not smaller than the resolution of the
detectors) while limiting partial voluming effects. Partial voluming effects can
impact on the ability to accurately determine the centre of the marker on images
which in turn can affect the accuracy of the point-based registration results83
.
1.2.11.3 Automatic registration techniques
Automatic image registration techniques use algorithms to perform registration based
on feature extraction84 or statistical analysis of the voxel intensities of the two image
data sets79. These techniques do not require points or surfaces to be identified
interactively by the user prior to registration. However even using automatic
processes, accurate feature extraction can be limited for low resolution and contrast
nuclear medicine images. Alternatively automatic image registration based solely on
the voxel intensities of the two image data sets eliminates any interactive or
intermediate steps in the registration process that can result in misalignment of the
images1, 78, 79
.
Voxel-based automatic image registration uses a similarity measure to evaluate the
alignment of the secondary image with the primary image. For a given
transformation (i.e. a set of translations and rotations) of the secondary image onto
the primary image, the similarity measure is calculated using the intensity level
information from both images. These calculations are most commonly performed
using only the overlapping data from the two images81. The two most common
33
similarity measures used in automated registration are cross-correlation (CC) and
mutual information (MI)85, 86. Both of these similarity measures can be selected for
use with the voxel-based automatic image registration software in the Syntegra
platform of the Pinnacle3 TPS87
Robust image registration using the CC similarity measure requires a linear
relationship between the grey scales of the two image data sets and is most often
used for registering intra-modality images (e.g. CT/CT or PET/PET registration).
The actual intensity values from each image are used to calculate the CC similarity
measure as shown in the following formula
.
81
.
−
=
∑ ∑
∑
Ω∈ Ω∈
Ω∈−
TBAA T
BAAAA
TBAA AA
X X X )(X
X XX
B - B)A (A
B - BAA
, ,
,
2)(
2
)( )(
)(
)( )( CC
τ
τ
where: A = Image A (primary image)
B = Image B (secondary image)
T = a given transformation or spatial mapping of image B onto A
AX = a voxel location within image A
)( AXA = the intensity value for a voxel at the location AX
B AXτ
)( = The intensity value for a voxel within image B relative to its
mapped position onto image A for a particular transformation T
BAAX ,Ω∈ = For all the voxel locations relative to image A in the overlap regions
of images A and B for a given transformation
A = The mean of the voxel intensities from image A within the overlap
domain of images A and B
B = The mean of the voxel intensities from image A within the overlap
domain of images A and B
The closer the CC similarity measure is to a value of 1.0 for a given transformation
of the secondary image onto the primary, the more likely it is that the images are
correctly registered.
34
The MI similarity measure is based on the probability distributions of the grey values
in each image85, 86, 88, 89. It does not rely on an explicit relationship between the grey
scales of the two images being registered (as does the CC technique), which makes
this similarity measure more appropriate for inter-modality image registration (e.g.
PET/CT registration). Essentially, the joint entropy of the probability distribution of
the intensity levels for both images is determined by the MI similarity measure. The
greater the reduction in the joint entropy of both images, the higher the probability
that a given transformation results in a true registration of both images88. The
Pinnacle3 TPS uses a normalised MI similarity measure as expressed by the
following equation87
.
∑
=
j,kfk
rj
Dj,k
Dj,k
PPP
VP 22
log- MI
where: V = the volume of overlap between the images
Prj and P f
k = the probabilities of grey value j and k in the reference and
secondary image respectively
P Dj,k2 = the probability that grey values j and k occur in the reference
image and at the corresponding position of the secondary
image.
There are a number of factors affecting the accuracy of voxel-based registration. As
discussed previously, different similarity measures are more suited to intra or inter-
modality image registration. Selecting the CC similarity measure to register a CT
and a PET image is inappropriate as there is no direct relationship between the
greyscales of the different features in these images. Other factors affecting the
robustness of automatic voxel-based registration are the techniques utilised by the
image registration software and image attributes.
When describing image registration processes it is important to note that
transformations can be either global or local. Global transformations are applied to
the entire secondary image: this is referred to as rigid registration1, 43. Local
transformations are used in deformable (or elastic) registration where different
35
rotations and translations are applied to sections of the secondary image. This warps
the secondary image to achieve a more accurate mapping of anatomical features from
this image onto the primary image. Deformable registration is useful when the
spatial relationships between anatomical features on the two images are not the same.
This may be due to the patient not being placed in exactly the same position when
each image is acquired or physiological factors such as respiration90, 91
.
Identical patient positioning for the planning CT scan and the whole-body staging
PET scan for accurate image registration has been previously discussed (see Section
1.2.9.5). Deformable registration software is not currently available in clinical
radiotherapy treatment planning systems such as the Pinnacle3 TPS. In the absence
of deformable registration software, local registrations cannot be applied to correctly
align regions where patient positioning may be different between images. Identical
patient positioning however, does not eliminate the differences that may occur in two
images due to patient respiration. Goerres et al92
performed a study which examined
using different breath-hold techniques to ensure correlated anatomy on both CT and
PET images acquired on a combined CT/PET scanner. However, for non-hardware
registered CT-PET images, a study could not be found in the published literature
which evaluated the affect which inter-image differences in patient internal anatomy
due to respiration have on the accuracy of rigid registration.
The approach and the techniques used for rigid registration software to iteratively
transform and evaluate the mapping of the secondary image onto the primary image,
is similarly described by various sources81, 85, 86, 88, 89
• Transformation of the secondary image onto the primary image
. These processes are:
Meas et al88
It is recommended that the initial transformation (the starting
estimate) is reasonably close to the “capture range” for optimal
registration (i.e. the overlapping of the two images most likely to
lead to optimal registration)
states that initial transformation should align the
centres and scan axes of both images.
81.
36
• Base image re-sampling
The two images being registered do not always have the same
voxel size so both images can be re-sampled to have the same
voxel size (cubic re-sampling)86
Meas et al
. 89
• Multi-resolution image sampling
describes a method of translating the voxel positional
and dimensional information in each image into co-ordinates to
avoid this re-sampling process.
The images can be re-sampled at a lower resolution (or larger grid
size) as a means of sub-sampling the images to increase the speed
of the registration process. Sub-sampling can be used to search
for an approximate alignment of the images88, 93
Like-wise the images can then be re-sampled at increasing
resolutions (smaller grid sizes) as a means of increasing the
accuracy of the registration
.
88
• Image interpolation
.
For any given transformation the voxels of the secondary image
may not be exactly aligned with those of the primary image,
particularly if there is a rotational offset between the images.
Interpolation of voxels surrounding a given point in an image is
required to determine the corresponding image intensity values
between the images89
Interpolation is also required for multi-resolution sampling to
determine corresponding intensity values between the images
.
86
• Evaluation of transformation using the similarity measure
.
The overlapping image data for a given transformation is
evaluated using the selected similarity measure to calculate a
value.
• Search optimisation
After the initial transformation, iterative transformations with
translational and rotational step sizes relative to the image
sampling resolution are performed until no further improvements
in the similarity measure values can be found. The transformation
37
with the most optimal similarity measure value is selected to
register the images85
Consideration is given to which of the different axes of the
secondary image are translated and rotated for each iterative
transformation
.
88
.
Due to the techniques just described, regions in images of extreme high or low
intensities (local maxima or minima respectively) can induce registration errors81
.
Local maxima or minima representing different patient anatomy can be incorrectly
aligned because their overlapping image voxels can be interpreted as optimal
solutions by the similarity measure algorithm. This effect can be seen when the
alignment of the images is outside of the capture range, most notably when there is
insufficient overlap of the images.
As voxel-based registration is based solely on the image data, attributes of the
images can also affect the registration outcomes. Images where a section of the
patient is scanned are referred to as truncated images. While planning CT scans
need to include sufficient anatomy for accurate treatment field localisation and dose
volume calculations, the smallest possible range of the patient is scanned to limit
unnecessary radiation exposure. Planning CT scan protocols for lung patients
stipulate the scan length starting at the intervertebral space between C7 and T1,
finishing at the base of the diaphragm. Studholme et al86
demonstrated that missing
image data at the top and bottom slices of MRI and PET brain images affects
registration accuracy.
Image spatial resolution, particularly slice thickness is another image attribute which
may impact on registration accuracy. A recent publication by Zhang et al94 on their
study evaluating four different voxel-based image registration algorithms discusses
the possibility of image registration accuracy being related to slice thickness.
Daisne et al’s95 evaluation of multi-modality registration concluded that the
accuracy of image registration is correlated to the spatial resolution of images.
However this study was conducted using a manual registration technique based on
interactive surface segmentation. It is also suggested that image registration
38
outcomes will be more robust when based on the transmission scan36, 90, 96, 97
due to
the transmission PET scan being more similar to the planning CT.
1.2.11.4 Validation of image registration
Validation of the image registration accuracy is crucial. Two validation methods that
are supported in the literature are the use of fiducial markers and visual inspection of
the registered images. Fiducial markers can also be used to verify imaged based
registration by providing a means of quality assurance82, 98. Partial voluming effects
of the imaged markers83 has potential implications for the use of fiducial markers as
a means of validating image registration. Wong et al99
performed a study to assess
the limits of visual detection of misregistration. A high level of anatomical detail
and similarity between the two images being registered assists greatly in achieving a
satisfactory level of certainty when assessing the accuracy of alignment of the two
images. Image interpretation skills are therefore an important part of the validating
registration results even when using automated techniques. Wong et al’s study used
registered PET and MRI images with introduced translational and rotational offsets.
Despite the lower spatial and contrast resolution of the PET images, observers were
able to detect offsets as small as 2mm and 2°.
1.2.12 Contouring techniques
1.2.12.1 Contouring techniques in a treatment planning system
Contours, also called regions of interest (ROIs), can be created in the treatment
planning process using a variety of methods depending on the available software.
They can be created by manually outlining a tumour volume or critical structure on
each 2D CT slice, relying solely on the user’s image interpretation ability to
delineate ROIs. The TPS combines the contours on each 2D CT slice to generate a
3D contoured volume for each ROI.
Automated techniques can also be used to create ROI’s. One such method uses a
range of CT numbers to create a 2D or 3D contour for a selected region or volume of
interest (VOI) on the CT image (see Figure 1-14). This method of contouring is
39
dependent on the selection of the CT number threshold levels. Incorrect thresholds
will not contour the required anatomical structure surface. It is most limited when
the boundaries between structures have similar CT numbers or where there is not a
continuous surface encompassing and separating a structure from its surrounding
anatomy. Threshold based contouring techniques can thus require manual editing.
Figure 1-14 Semi-automated threshold contouring techniques (a) To determine CT number threshold levels for automatic contouring of a structure (in this case the spinal canal) a profile of CT numbers is generated. The red lines on the CT number profile indicate the range of CT numbers within the spinal canal. (b) When a range of CT numbers from 900 – 1200 is used the spinal canal is correctly contoured. (c) Incorrect contouring as there is not a continuous surface of the spinal canal.
(a)
(b) (c)
40
3D model-based contouring creates contours using 3D mesh models of organs or
structures to adapt to individual patient dimensions on a CT data set. Model-based
contouring can be used to create models of regions of interest that can be used to
provide consistency of outlining from one user and data set to another100. Model
based segmentation (MBS) software in the Pinnacle3
TPS uses CT numbers as one of
the parameters for adapting the model. It also provides rules on the maximum
distance it can change from its original shape (or organ flexibility), the permissible
CT number gradient at the edge of the organ or structure being outlined, and whether
it has a positive or negative gradient. Once the mesh has been adapted it is converted
into contours on each slice which are added together to create a 3D volume.
1.2.12.2 Reported methods of GTV contouring on PET images
Various GTV contouring methods using registered CT/PET images have been
reported in the literature. These include manual techniques relying solely on visual
inspection of the registered images101 as well as various threshold methods based on
the PET image data5, 36, 37, 70, 71, 102, 103
. Threshold methods are used for deriving PET
based-contours as there is not an abrupt jump in intensity levels at the boundary of a
region of high uptake mostly due to image resolution. Different threshold values
(using either the pixel values or SUV data within a volume of interest on the PET
image) have been described in the literature (see Table 1-2).
Table 1-2 Threshold methods for contouring a GTV using the PET image data
Author Threshold method
Mah et al37 50% of the maximum intensity within a VOI
Erdi et al36 42% of the maximum intensity within a VOI (>4cm 3
Bradley et al
volumes only)
71 40% of the maximum intensity within a volume
Black et al102and Grills et al103
[(0.307x mean SUV) + 0.5853] where mean SUV = the mean SUV within a VOI
Paulino et al5 SUV > 2.5 determines the VOI edge
The recommendation that an absolute threshold value of SUV > 2.5 be used to define
the edge of a GTV5 appears to be based on Patz et al’s104 evaluation of SUV’s as an
41
indicator of the malignancy of pulmonary abnormalities on PET images. Black et
al’s102 phantom study evaluated the effect of volume, variation in 18
F-FDG
concentration within a volume and background activity on contouring outcomes
based on a VOI mean (described in Table 1-2). It was found that a VOI mean SUV <
2 affected the accuracy of their threshold method, however volume size and
background activity were found to have no effect.
Erdi et al105 tested a method for accurate segmentation of lesions in the lung for PET
images. An adaptive threshold method was found to produce accurate volumes,
where the threshold level used varies depending on the source-to-background activity
and the size of volumes. While the purpose of this study was to determine accurate
volumes of lesions on PET images for more effective administered dosages for
radionuclide therapies, it has potential implications for threshold levels for accurate
GTV definition in radiotherapy treatment planning. Threshold levels of 10-20% of
the VOI maximum were found to accurately contour a moving sphere in a phantom
study70
. These results are significantly different from the previously mentioned
studies which were performed on patient images in which the PET images would
have been acquired under the influence of respiration. Which of the described
contouring techniques for PET-determined GTVs can provide the most efficient
approach as well as accurate volumes for radiotherapy treatment planning is
uncertain.
When considering the implementation of any contouring protocol in radiotherapy,
techniques to reduce inter-user variation are important. The use of an accurate
threshold technique may be negated by inconsistencies in the protocol which result in
high inter-user variation. Riegel et al106 found that an institutional protocol for
contouring on registered CT/PET images was required as the use of registered
CT/PET images in treatment planning without contouring protocols did not eliminate
differences between observers. While Mah et al37 observed a reduced rate of inter-
observer variability in tumour definition using CT/PET registered images, they also
concluded that more uniform GTV definition protocols for the radiation oncologists
were required. The inclusion of appropriate viewing windows as part of a CT/PET
contouring protocol was found to reduce inter-user variation107, demonstrating the
importance of the manipulation of the images within image fusion software.
42
1.3 Aims of the project
1. To perform a quality assurance study of the processes associated with using
registered CT and PET scans for tumour volume definition for radiotherapy of
lung cancer patients based on CT and PET images of a phantom. This quality
assurance study will be used to
• Investigate image acquisition and manipulation techniques for registering
and contouring CT and PET images in a radiotherapy treatment planning
system.
• Determine technology and image-based errors in the registration and
contouring processes of these images.
2. To use the outcomes of the phantom image based quality assurance study to
determine clinical protocols for:
• Acquiring patient PET image data for incorporation into the 3DCRT
process.
• CT/PET registration.
• GTV definition.
3. To test the developed clinical protocols to assess levels of accuracy and
reproducibility attributed to the use of these protocols. This will be achieved by
the participation of radiation therapists and radiation oncologists in image
registration and GTV definition trials using patient images
43
1.4 Ethical considerations
There were no ethical considerations for any stage of the research involving the use
of the phantom. Standard protocols regarding radiation safety and workplace health
and safety were adhered to when PET and CT scanning the phantom. The target
delineation and registration protocol development using the PET and CT data for the
phantom did not require the participation of any individuals other than the principal
researcher.
Acquisition and use of phantom data for determination of image registration
and target volume delineation protocols
The NHMRC guidelines
Acquisition and use of patient data, and RT and RO participation for the image
registration and target volume definition protocol trials 108
1. The use of patient PET and planning CT scans and
for research involving data collection and participants
were adhered to. Ethics approval was sought for:
2. The RT and RO participation in the image registration and the GTV
delineation trials.
Submissions were made to both the Princess Alexandra Hospital’s and the
Queensland University of Technology’s Human Ethics Research Committees
(HREC). While the research was conducted in collaboration with the Mater
Radiation Oncology Service at the Mater Hospital, this service is under the
management of the PAH. A full HREC application was made to the PAH HREC
with a Level 2 (expedited) HREC application was made to QUT’s HREC. Approval
was granted by both committees.
It needs to be noted that the patient data was not acquired with the intent to intervene
in standard practices involved in the patients’ diagnosis and treatment planning
procedures. The patients received no extra radiation exposure as no scans were
performed solely for the purpose of this research. Patients whose data might be able
to be used for the purposes of this research were identified at clinical presentation
and were followed through their routine staging and planning procedures. This was
Patient data acquisition
44
done as patient data would be used for the RT image registration and RO GTV
delineation trials if they met the following criteria:
1. Only patients that might be a candidate for radiation therapy, requiring a
routine diagnostic staging PET scan, were targeted for potential use of their
PET data.
2. Only patients who would need a routine planning CT scan performed as part
of their treatment planning procedure prior to treatment delivery.
3. Only patients who were positioned in the same position for their PET scan as
their planning CT scan.
In order to satisfy criteria 3 for patient data use for the study, some patients were
PET scanned on a flat couch top with the appropriate stabilisation devices and their
arms up. Wide consultation with referring specialists, nuclear medicine
technologists, physicists and radiologists determined that this would have no impact
on the outcome of the PET scan for diagnosis and staging purposes. The stabilisation
equipment would not produce image artifacts. Staging scans for lung cancer patients
were routinely performed with the patient’s arms up or down. Advice was sought as
to whether there were any ethical concerns regarding the patient positioning at the
PET scanning stage and no concerns were raised as there would be no deviation from
routine protocols for PET scans for lung cancer staging.
Initially it was thought that fiducial markers should be used during the patients’
routine staging PET scan to provide a means of reference for analysis of the
registration protocol trials involving the RTs. However these PET scans were used
by a number of medical specialists for determining the patients’ treatment
management. It was felt that the fiducials could very easily be mistaken for
pathology and thus have the potential to alter the patients’ clinical management due
to a false diagnosis. The risks to the patient outweighed any benefits of using the
fiducial markers for this research and it was decided not to attempt to use them for
the patient PET scans. Other means of analysing the outcomes of the RT registration
trials were sought.
45
Patient consent was not required for use of their PET and planning CT scans in the
image registration and GTV delineation trials. These scans were part of the patient’s
routine clinical staging and treatment planning procedures and were to be used
retrospectively after the patient had finished their treatment planning. It was
considered routine clinical patient data and approval was given to use the data if it
was suitably de-identified so that the data could not be connected to the individual
patient. The patients’ medical history (i.e their disease staging and the PET scan
report from their radiotherapy charts) was used to provide information to the RO’s
for the GTV delineation study. This information was also de-identified prior to the
commencement of the study as per ethics approval requirements.
Use of patient PET and CT data in trials
As the data was being used retrospectively, consideration needed to be given to
protect the professional integrity of the radiation oncologists and radiation therapists
involved in the planning process of these patients. Any information that could be
identified with a patient and their treatment process, including any health practitioner
involvement, was removed from the patient data used.
The ROs and RTs were classed as participants in the image registration and GTV
delineation trials and there were some ethical considerations that needed to be
addressed. Approval was given for these trials as any risks associated with
participation were identified and addressed where necessary.
RT and RO participation in the image registration and GTV delineation
protocol trials
An identified potential risk to the participants associated with the trials was if
individual participants could be identified with their performance. In this situation
the participant may have felt that their professional reputation could be
compromised. To avoid this, prior to commencement of the study, all participants
were assigned an identifying code that was known only to the participant and the
principal researcher.
There were concerns that the time commitment by participants might be excessive.
This was particularly an issue with the RO involvement. Initially it was thought that
46
the ROs would 1) contour the GTV on the CT alone, then 2) contour on the
registered CT/PET data without protocols and 3) use the registered CT/PET data
with contouring protocols. The procedure of the GTV delineation trial was reviewed
to keep the total time participation within what was considered reasonable. It was
decided to eliminate the second step of contouring on the registered CT/PET data
without the protocols.
Participation was voluntary. Participants were required to read the participant
information document and sign a consent form with the understanding that they
could withdraw from the trials at anytime.
1.5 Research agreement
It was a requirement that a research agreement be drawn up between the PAH and
QUT concerning the RO and RT trials. The standard research contract between the
PAH and QUT was used as the basis of this agreement.
47
2
2.1 Phantom design and construction
Image acquisition and analysis
2.1.1 Aims of phantom construction
To design a phantom that would simulate the chest region of a patient with the
effects of air in the lungs, a lesion or nodes in the mediastinum and a moving lesion
in the lungs. Specifically the phantom was required to provide CT and PET images
that could be used to:
1. Investigate the effects of the different spatial resolutions and voxel sizes of CT
and PET on imaging and contouring objects of different size. It was anticipated
that the known geometry of the phantom and the superior spatial resolution of the
CT images could be used as a template for comparing with the PET images.
2. The effects of increasing background uptake on the ability to image different
sized objects with PET accurately.
3. The effects of motion on an imaged object for both PET and CT images.
The phantom also needed to produce minimal artifacts and needed to fit through the
apertures of both the CT and PET scanners, allowing for the full dimensions of the
phantom to be scanned within the maximum FOV.
48
2.1.2 Phantom dimensions and functions
A two-part phantom was specifically designed and constructed for the purposes of
this research. It consisted of a main tank and a variable speed motor-driven moving
sphere that could be placed in the phantom as required.
2.1.2.1 Main tank design
The main tank was constructed entirely of Perspex to avoid artifacts from metal in
both the CT and PET images. Ideally the phantom would have been constructed with
no sharp edges to better simulate the shape of the chest but this would have been cost
prohibitive. Figures 2-1 and 2-2 illustrate the design of the main tank.
Figure 2-1 Main tank design: INF / Feet view A=Main tank, B=2.0 cm central rod, C=1.0 cm central rod, D=0.5 cm central rod, E=1.5 cm central rod, F=3.0 cm central rod, G and H=air cavities. (NB Drawing not to scale).
= mid-tank sagittal plane
A
B
C
D
E
F
G
40 cm
40 c
m
H
49
The functional features of the main tank are:
Region A: The main tank which could be filled with water and/or 18
F-
FDG. There were two regions of air on either side of the tank.
Its maximum dimension of 40 cm was chosen as this was a
reasonable size for an adult male’s chest and also as a result of
the 56 cm bore size on the PET scanner that was to be used for
the study.
Regions B-F: Isolated rods within the main tank which could be filled with
water and/or 18
F-FDG. This allowed different ratios of
activity per ml in these rods to that used in the main tank as
required.
Regions G and H: Region G and H were to simulate the air cavities of the lungs.
Region H was left open on one side to facilitate easy and
greater flexibility in the positioning of the moving sphere
within the main tank.
50
Figure 2-2 Main tank design: Side view at the mid-tank sagittal plane A=Main tank, B=2.0 cm central rod, C=1.0 cm central rod, D=0.5 cm central rod, E=1.5 cm central rod, F=3.0 cm central rod. (NB Drawing not to scale).
The volume in ml of regions A to F were determined (see Table 2-1) so that different
ratios of activity per ml could be calculated and injected into the main tank of the
phantom compared to the isolated central rods. This would simulate different
background to tumour uptake ratios.
A
B
C
D
E
F
SUP / H
ead
INF / Feet
200mm
51
Table 2-1 Capacity (ml) of different compartments in phantom Total ml in compartments B-F = 254 ml
Phantom Capacity
Compartment Diameter of central rods Capacity (ml)
A Main tank N/A 12500 ml
B 3.0 cm 143 ml
C 1.5 cm 34 ml
D 0.5 cm 3 ml
E 1.0 cm 15 ml
F 2.0 cm 59 ml
2.1.2.2 Moving sphere design
A hollow Perspex sphere that allowed various liquids to be injected into it for
imaging on a CT and PET scanner was required. This sphere needed to oscillate
along a fixed trajectory within the body of the existing phantom at 15 to 20 cycles
per minute. To enable this to happen, a motor and appropriate supporting structures
for the sphere were required.
The final design of the moving sphere is shown in Figure 2-3. Specific features of
the moving sphere were:
• The outer sphere diameter = 4.5 cm
• Inner sphere diameter = 3.5 cm
• Capacity of sphere = 24 ml
• A variable speed motor was used so that different respiration cycles of a
patient could be used when using the sphere to simulate a lung lesion.
• The trajectory of the sphere was straight not elliptical.
• The maximum displacement of the sphere along its path of trajectory was 2.0
cm.
• A major design specification was to ensure that the motor driving the sphere
was at a sufficient distance from the sphere so that it would always be outside
of the FOV for all the scans
52
• The mount of the sphere was specifically designed so that it could be placed
at various positions and angles within the main tank of the phantom.
Figure 2-3 Photograph of moving sphere, including mount and motor (a) = Hollow sphere mounted on rod with variable speed motor to simulate respiratory motion (b) =Demonstration of the variable angulation of the rod and hence possible variation in direction of the moving sphere relative to the scan plane (c) = Positioning of sphere and mount relative to the main tank. The arrow indicates the direction of the motion of the sphere.
(a) (b)
(c)
53
2.2 Phantom image acquisition and analysis
2.2.1 Aims
1. To acquire CT and PET images of the constructed phantom which simulate
different patient specific conditions that could be used to investigate image
registration and GTV delineation processes. This was to provide data for:
• Determining whether PET AC emission scans or, AC emission and
transmission scans were required to provide more effective image
registration outcomes.
• The effects of using free breathing CT and PET scans on image
registration outcomes.
• The effectiveness of fiducial markers as an aid in CT/PET registration.
• Determining the effect of increasing background to lesion ratios on
threshold levels for ROI geometric edge detection for GTV delineation.
• Determining the effects of motion on threshold levels for geometric edge
detection for GTV delineation.
2. To perform baseline volume measurements on the CT scans of the phantom using
the contouring tools in the treatment planning system. The baseline contours will
be converted into 3D models of the different features of the phantom that can be
loaded onto the PET AC images to:
• To evaluate the level of accuracy that may be achieved for PET-based
contours.
• To extract image data for different volumes of interest on the PET AC
scans.
3. Investigate image manipulation and quantification in a treatment planning system
to ensure accurate geometrical visual representation of the PET images as well as
the validity and appropriate use of the PET image data.
4. Investigate the effects of respiration-induced motion on an imaged volume for
both CT and PET images so that these effects can be factored into the image
registration and contouring processes that are to be investigated.
54
2.2.2 Methodology
2.2.2.1 Phantom CT scan acquisition
All of the CT scans of the phantom were performed on a Siemens Somatom “Open
Sensation” 20 slice, wide bore CT scanner.
A plastic template was made which had the position of the main tank and the moving
sphere support mount drawn on it (see Figure 2-4). This was to ensure a
reproducible and efficient set up of the two components of the phantom when the
sphere was required to be imaged with the main tank. The main tank was positioned
with the inferior/feet end of the tank facing the bore of the scanner. The lasers on the
CT scanner were aligned with the mid-tank sagittal and transverse planes drawn on
the main tank.
Phantom set up on the scanner
Figure 2-4 Photograph of phantom set up for scanning Main tank of phantom with moving lesion, both mounted on base template
55
When the moving sphere was required for a scan it was placed so that the motor and
mount were at the inferior/feet end of the phantom. The sphere’s position relative to
the main tank is shown in Figures 2-5 and 2-6.
Figure 2-5 Moving sphere scanning position in main tank: INF / Feet view A=Main tank, B=2.0 cm central rod, C=1.0 cm central rod, D=0.5 cm central rod, E=1.5 cm central rod, F=3.0 cm central rod, G and H=air cavities, I=moving sphere (drawing not to scale).
= mid-sphere sagittal plane
The black circle (Region I) as shown in Figures 2.5 and 2.6 demonstrates the position
of the sphere relative to the tank when it was static. The red and blue circles in
Figure 2-6 show the path of oscillation of the sphere and the maximum displacement
relative to the superior /inferior directions of the phantom.
A
B
C
D
E
F
G H 8.5cm
I
56
Figure 2-6 Central sphere sup/inf scanning position in main tank: side view at the mid-tank sagittal plane A=Main tank, I=position of sphere when static=central sup/inf position of oscillation (drawing not to scale).
IZI MM3003 multi-modality fiducial markers were used (see Figure 2-7). The
markers consist of a sealed gel ring that is visible on CT images. There is a cavity in
the centre of the ring for injected radioactive pharmaceuticals for nuclear medicine
imaging. Exact manufacturer specifications of the markers are:
Use of fiducial markers
• 15 mm outer diameter
• 3.5 mm thick
A
Mid transverse plane
Mid–sphere coronal plane
SUP / H
ead
INF / Feet
Centre of sphere max. superior displacement = 1cm
Centre of sphere max. inferior displacement = 1cm
5.5cm from bottom of sphere to main tank
10.0cm
Perspex rod sphere mounted on
I
57
• 5 mm axial hole (http://www.izimed.com/mmm.asp)
Figure 2-7 MM3003 multimodality fiducial markers
The multimodality fiducial markers were placed on the top of the phantom on both
the left and right sides and were aligned with the mid transverse plane (half the
length) of the phantom. The fiducial marker positions are indicated in Figure 2-8.
Figure 2-8 Position of fiducial markers on phantom
A series of CT scans of the phantom were acquired with all components static. The
planning CT scans for lung cancer patients are performed with the patient head first
into the scanner, with the table moving into the scanner during the scan, scanning the
patient in a superior to inferior direction. The specific CT scanning procedure,
including phantom set up, for this series of scans was:
Static CT scanning of the phantom
Sealed gel ring with adhesive backing
Injection site for 18-FDG
Fiducial positions on phantom
58
• The base plate with the main tank alone with and without the sphere was
placed so that the superior/head aspect of the main tank was facing the
bore of the scanner.
• MM3003 fiducial markers were placed on the main tank in same positions
indicated in Figure 2-8.
• The base plate was positioned so that the transverse laser on the scanner
was aligned with the mid-transverse plane of the main tank. (This was
also through the centre of both fiducial markers)
• The scanner zeroed at the mid-transverse plane of the markers
• “Patient” position:
Supine
Head first
Table moves into scanner
• Scan length: This was selected to ensure a minimum of 1 cm overshoot of
the main tank at both the superior/head and inferior/feet aspects.
• 3 mm or 5 mm slice thickness
• Field of view (FOV) = 750 mm
• The standard chest imaging protocol that was used for all lung patients
was selected:
70 mAs
120 kVp
Pitch = 1.2
• Image reconstruction
512 x 512 mm matrix size
Siemens adaptive multiplanar reconstruction (AMPR) algorithm
Table 2-2 indicates the specific conditions of each of the acquired “static” CT scans
of the phantom.
59
Table 2-2 The “static” phantom CT scan acquisition parameters Scan no. Conditions of scan acquisition
1 The main tank alone with the central rods filled with water and the main tank empty. Slice thickness = 5 mm
2 The main tank alone with both the central rods and the main tank filled with water. Slice thickness = 5 mm
3 The main tank with the central rods filled with water, the main tank empty and the sphere filled with water in the static position. Slice thickness = 3 mm
A series of CT scans to represent a “free breathing” scan of a lung cancer patient
were performed on the phantom. The scanning procedure and parameters for this
series of scans was exactly the same as for the static scans of the phantom except the
sphere was scanned moving at different rates and a slice thickness of 3 mm was
selected all of the scans. Table 2-3 lists the details of each scan series.
“Free breathing” CT scans of the phantom
Table 2-3 Phantom conditions for the “free breathing” phantom CT scans Series no. Conditions of scan acquisition
Series 1 5 scans with the central rods filled with water, the main tank empty and the sphere filled with water oscillating at 15 cycles/min.
Series 2 5 scans with both the central rods and the main tank filled with water, and the sphere filled with water oscillating at 15 cycles/min.
Series 3 5 scans with the central rods filled with water, the main tank empty and the sphere filled with water oscillating at 20 cycles/min.
Series 4 5 scans with both the central rods and the main tank filled with water, and the sphere filled with water oscillating at 20 cycles/min.
A 4D CT scan was required so the 4D volume of the sphere could be measured from
CT to use as a reference for measuring the effects of motion on the threshold values
for geometric edge detection on the PET scans of the moving sphere. The CT
scanner used did not have 4D CT capabilities so a 4D scan was simulated. The
procedure and scan parameters were exactly the same as for the static CT scans of
Simulation of a 4D CT scan of the moving sphere
60
the main tank and the sphere except the sphere position was off-set from the central
position in 5 mm increments. Five CT scans of the phantom were performed with
the sphere static but in each of the different positions shown in Figure 2-9.
Figure 2-9 Scanning positions of the sphere to simulate a 4D CT volume of the moving lesion
0cm displacement of sphere 0.5cm sup displacement of sphere 1.0cm sup displacement of sphere -0.5cm inf displacement of sphere -1.0cm inf displacement of sphere
SUP / H
ead
INF / Feet
Mid transverse plane of main tank
Mid–sphere coronal plane
61
2.2.2.2 Phantom PET scan acquisition
All of the PET scans of the phantom were performed on a Philips Allegro stand-
alone PET scanner. The Allegro PET scanner utilises GSO detectors with a 137
Cs
gamma source for transmission imaging.
2.2.2.2.1 Preliminary phantom test PET scans
A series of preliminary test scans on the main tank phantom were performed to
determine a suitable activity to inject into the phantom. The SI unit for activity, the
Becquerel (Bq) was used. The aim was to find a suitable activity per ml (Bq/ml) that
would provide equivalent image quality as seen in clinical studies using the same
scanning protocol used for scanning lung patients. In particular the individual rods
needed to be well visualised. The decay of the activity of the
Determination of activity for PET scanning phantom
18
F-FDG was taken
into account for any time that may elapse between injection of activity and scan time
to ensure the Bq/ml at scan start time was known. The phantom was scanned:
1. With 30 MBq in 11000 ml of water in the main tank of the phantom (0.0027
Mbq/ml) with no water or activity in the central rods.
2. With 1 MBq in 254 ml in the central rods (0.0039 MBq/ml).
3. With 5 MBq in 254 ml in the central rods (0.0197 MBq/ml).
4. With 5 MBq in 254 ml in the central rods and 12.3 MBq in the main tank
(0.0197 MBq/ml in all compartments).
A qualitative method of assessing the correct activity/ml for the phantom was used.
The standard nuclear medicine window levels for viewing patient scans were applied
to the images and the visibility of the central rods was then assessed. Several
different concentrations of activity were trialled until all the central rods could be
clearly seen, with and without background.
Part of the GTV delineation study was to determine if SUV’s and/or a percentage of
the maximum uptake in a ROI could be used to determine thresholds for geometric
edge detection. The Pinnacle
SUV determination
3 treatment planning system is able to read the SUV’s
62
of a PET scan only if an SUV study has been performed. The Philips Allegro PET
scanner can determine SUV on AC images if the weight of the subject and the
injected activity are entered at the same time as the patient data and scan parameter
prior to initialisation of the scan. The water volume (12500 ml) of the main tank was
weighed as 13 kg. This was entered as the subject weight. The activity at the time it
was initially drawn up for injection was recorded and entered for each scan.
The phantom set-up on the PET scanner was the same as for when the CT scans of
the phantom were performed. When the sphere was scanned, the set up of the main
tank and sphere relative to each other was the same as for the CT scans using the
base plate to ensure reproducibility. MM3003 fiducial markers were placed on the
main tank in same positions as for the CT scans (see Figure 2-8). The markers were
injected with
PET scan parameters
18
• A 20 ml syringe was drawn up with a solution of saline and 2 MBq of 18-
FDG (see Figure 2-10a). This solution resulted in a concentration of
activity of 0.1 MBq/ml.
F-FDG using the following method:
• A 27 gauge needle or smaller was used to inject the saline-18
• The needle was withdrawn slowly at the angle of injection.
F-FDG
solution. The needle had to be inserted sideways through the gel into the
cavity (see Figure 2-10b). Only a few drops of the solution were required
to fill the marker cavity. Too much resulted in the solution leaking
through the injection site (see Figure 2-10c).
Figure 2-10 Fiducial marker injection technique
(a) (b) (c)
Lung cancer patients were routinely scanned with a whole body AC protocol,
positioned feet first into the scanner. The table is the moved out of the scanner as the
63
patient is scanned from head to feet. The entire length of the phantom was scanned
with the scan start position above the superior/head aspect of the phantom. The same
clinical scanning and image reconstruction protocols that would typically be used for
PET scanning lung cancer patients were used for scanning the phantom. These were
as follows:
• Patient weight: 13 kg (weight of main tank of phantom full of water)
• Patient orientation : supine feet first table direction out
• Scan length = 264 mm
• FOV = 576 mm
• 2 emission frames: scan time=3 mins/frame
• 4 transmission frames: scan time=1.54 mins/frame
• Image reconstruction
144 x 144 mm image matrix size
Reconstruction algorithm = body/row-action maximisation-
likelihood algorithm (RAMLA)3D/3D AC/SUV
2.2.2.2.2 Different phantom PET scan conditions for use in image registration
and GTV delineation protocol testing
The phantom was PET scanned with different main tank Bq/ml to that in the central
rods and the sphere. The different PET scanning conditions of the phantom are
detailed in Table 2-4. All of the PET scans were performed with fiducial markers.
Scans were performed with decreasing TBRs (i.e. increasing levels of main tank
activity relative to the central rods). When the sphere was imaged it was either static
or oscillating at 20 cycles/min. Six scans of each condition were performed as this
would give different levels of emitted counts for each image, due to the rate of decay
of the initial activity for each image.
The injected concentration of 18F-FDG activity in water was maintained as 0.0197
MBq/ml in the central rods and the sphere for the different series of PET images,
except for those in series 2, where 1 MBq was injected into each rod. This resulted
in different concentrations of activity of 0.3333 MBq/ml, 0.0667 MBq/ml, 0.0294
MBq/ml, 0.0169 MBq/ml and 0.0069 MBq/ml for the 0.5 cm, 1.0 cm, 1.5 cm, 2.0 cm
and 3.0 cm central rods respectively.
64
Table 2-4 Different PET scanning conditions of the phantom for images to be used to test image registration and GTV delineation protocols The rate of decay formula was used to calculate the activity initially required to take into account elapsed time from the drawing up of activity for injection to the initialisation of the PET scan. This ensured that there would always be the same activity in the phantom at the initialisation of each scan.
*Activity increased to keep same Bq/ml due to additional 24 ml in sphere
Condition /Series Sphere Target-background ratio (TBR)
MBq / rod (+sphere where applicable)
volume (ml)
MBq / tank volume (ml)
Total scans Name of scan
1 - 0% background activity 5MBq/254 ml 0/12500 6 1a 1b 1c 1d 1e 1f
2 - 0% background activity 1MBq in each rod 0/12500 6 2a 2b 2c 2d 2e 2f
3 Static 0% background activity 5.5MBq/278 ml* 0/12500 6 3a 3b 3c 3d 3e 3f
4 Moving 0% background activity 5.5MBq/278 ml* 0/12500 6 4b 4c 4d 4f 4g 4h
5 - TBR = 20 (main tank = 5% activity of rods) 5MBq/254 ml 12.3/12500 6 5a 5b 5c 5d 5e 5f
6 Moving TBR = 10 (main tank = 10% activity of rods) 5.5MBq/278ml* 24.6/12500 6 6a 6b 6c 6d 6e 6f
7 Moving TBR = 5 (main tank = 20% activity of rods) 5.5MBq/278 ml* 49.2/12500 6 7a 7b 7c 7d 7e 7f
8 Moving TBR = 2.5 (main tank = 40% activity of rods) 5.5MBq/278 ml* 98.4/12500 6 8a 8b 8c 8d 8e 8f
65
2.2.2.3 Image quantification using the treatment planning system tools
The images of the phantom were exported from the CT and PET scanners in Digital
Imaging and Communication in Medicine (DICOM) format and burnt onto CD
before being imported into the Philips Pinnacle3
treatment planning system. Both the
PET AC and transmission scans, were imported.
The inner volumes of the rods and the inner and outer volumes of the static sphere
were calculated based on the known geometry of these objects in the phantom.
Baseline 3D contours were then generated in the TPS for the CT scans of the
phantom using the scan 3 of the phantom from the “static” CT scan series (see Table
2-2). The visible inner dimensions of the rods and the inner and outer dimensions of
the static sphere were contoured on the axial slices of the image using a semi-
automated thresholding technique to detect either the air/perspex or water/perspex
interfaces (see Fig 2-11). A volumetric 3D contour was automatically generated by
the TPS software based on each axial slice’s contour for a region of interest.
Baseline contouring of the CT images of the phantom
Models of the baseline contours of the central rods and the sphere were created for
extracting image pixel and SUV data from the PET images of the phantom. These
were created by converting the baseline 3D contours into 3D models using the MBS
tools in Pinnacle3
• 3D threshold-generated contours of each of the separate central rods.
. These models were then loaded and positioned on the CT image
and converted back into a 3D contour to assess the level of accuracy of 3D model
generated contours. Using the TPS ROI statistics tools, the following volumes were
computed:
• 3D threshold-generated contours of the inner and outer dimensions of the
sphere.
• 3D model-generated contour of each of the separate central rods.
• 3D model-generated contour of the inner and outer dimensions of the
sphere.
66
Figure 2-11 Contouring of the central rods on scan 3 from the “static” CT scan series of the phantom Image (a) demonstrates the threshold-based contouring for both the central rods and the static sphere. Image (b) demonstrates the threshold-based contouring for both the outer and inner dimensions of the static sphere.
(a)
(b)
Volumes of the moving sphere (inner and outer dimensions) were calculated based
on its known geometry and range of motion. To visually assess the impact of motion
on both the free breathing CT and PET AC scans in the absence of 4D scanning
technology, the TPS contouring tools were used to generate a contour representing
the 4D volume of the moving sphere. These were created by:
Creation of a 4D model of the moving sphere
• Registering the CT scans with the sphere scanned statically in the 5
different positions encompassing its full range of motion.
• Placing the previously determined 3D model of the inner dimension of the
static sphere on the imaged position of the sphere on each of 5 scans and
combining them to produce a 3D contour that encompassed the sphere’s
range of motion (Figures 2-12a to 2-12e).
• The contour of the 4D volume was then converted into a 3D model using
the MBS tools.
67
• This method was repeated to obtain a 4D volume of the outer dimensions
of the sphere (Figure 2-13f).
• The volume of each of the contours and models of the 4D volume of the
moving lesion were calculated using the TPS tools.
Figure 2-12 Creation of the 4D models of the moving sphere The 3D model of the inner dimensions of the static sphere has been placed on the, (a) Static scan with the sphere displaced 10mm sup (red contour), (b) Static scan with the sphere displaced 5mm sup (green contour), (c) Static scan with the sphere displaced 0mm (blue contour), (d) Static scan with the sphere displaced 5mm inf (purple contour), (e) Static scan with the sphere displaced 10mm inf (orange contour), (f) The 3D model of the outer dimensions of the static sphere has been placed each of the static scans (the scan with the sphere displaced 0mm is shown).
(a) (b) (c)
(d) (e) (f)
68
Each of the separate 3D models of the rods was loaded onto each AC emission scan
in each of the different series of the PET phantom images. Each model was
converted into a 3D contour (see Figure 2-13) and their volumes were computed
using the TPS ROI tools. These values were compared with the calculated and the
3D model generated volumes on the CT images of the phantom.
Contouring of the PET AC images of the phantom
Figure 2-13 3D models positioned and converted to contours on the PET AC images of the phantom (a) Transverse view of the phantom with the 3D models converted to contours for the central rods. The light-green circle is the 3D spherical contour used to determine the pixel and SUV data in the main tank. (b) Sagittal view of the phantom demonstrating the 3D models converted to contours for the central rods.
(a) (b)
The CT/dose tool in the Pinnacle
Quantification and validation of PET pixel and SUV data in the TPS 3 TPS was used to determine the PET AC image
data that could be obtained in the planning system (see Figure 2-14). There were two
values for each pixel of the PET AC emission images; the first was a value with no
indicated units and the second was clearly the SUV data. The expected density value
of the water and 18F-FDG solution was 1.0 g/cm3, not the 0.84 g/cm3
given by the
TPS for the pixel selected in the middle of the 2 cm rod.
69
Figure 2-14 PET AC image pixel data available using the CT/dose tool The cursor was clicked in the middle of the 2 cm rod (as indicated by the yellow arrow) which was filled with the water and 18
F-FDG solution. Two values for the selected pixel within the 2 cm rod were given, an actual pixel value of 8210 and an SUV of 38.87.
The ROI statistic tools were used to obtain the minimum, maximum and mean pixel
and SUV value within each 3D contour of the rods and the sphere (where imaged) on
the AC emission images. Background pixel and SUV values for series 5 – 8 were
obtained by positioning a 3D contour of a sphere in the main tank of the phantom
(the light-green contour in the main tank of the phantom in Figure 2-13a).
2.2.2.4 Determination of window width and level for PET images
Scan 2 from the “static” CT scan series was registered with each of the images (both
AC and transmission) from the PET scan series 1, 2 and 5 of the phantom in the
Pinnacle3
TPS. Static CT Scan 4 was registered with the images from the PET scan
series 3, 4 and 6 to 8.
The window widths and levels were adjusted on each of the PET images:
• On the AC scans until:
70
1. The regions of uptake in the rods and the main tank visually
matched the inside dimensions of the rods and main tank on the
CT image (the inside dimensions of these features correspond to
the injected 18
2. The regions of uptake in the central rods visually matched the
inside dimensions of the rods in the CT scan. This resulted in the
background being windowed out for the PET AC images from
series 5 -8). This is demonstrated in Figure 2-15b.
F-FDG fluid volume on the PET AC images). This
is demonstrated in Figure 2-15a.
• On the transmission scans (see Figure 2-15c) until
3. The outer dimensions of the main tank matched those on the CT
scan
The window width and levels of the PET scans that resulted in the required
visualisation of the different features of the phantom were recorded for both the AC
emission and transmission scans for each series of the PET scans.
To determine the window widths and levels for visualising the static or moving
sphere on the PET AC scans in series 3, 4 and 6 to 8, the model of either the static or
4D volume of the sphere was loaded onto the CT image over the known coordinates
of the sphere’s centre. The window width and level of the PET image was adjusted
so that the dimensions of the sphere on the PET image visually matched the model of
either the static or 4D volume of the sphere (see Figure 2-16).
71
Figure 2-15 Visualisation of the methods used for determining appropriate window width and levels for viewing PET images Images (a) – (c) show transverse, sagittal and coronal views of the phantom with the registered images of CT static scan 2 and PET series 5 (a) Demonstrates the registered PET AC and CT scans displayed as a blending of the two images. The PET AC scan has been windowed so that the background and rods are visible, with the dimensions of the high uptake of the central rods matching the inner dimension of the rods on the CT image. (b) Demonstrates the registered PET AC and CT scans with the CT image displayed partly with a cut-out showing only the PET AC image along one half of the central rods. The PET AC image has been windowed so that only the rods are visible, again with the dimensions of the high uptake of the central rods matching the inner dimension of the rods on the CT image. (c) Demonstrates the registered PET transmission images and CT scans with the CT image displayed partly with a cut-out showing only the PET transmission image along the inferior half of the main tank of the phantom. The PET transmission scan has been windowed so that the outer dimensions of the main tank on the phantom match those on the CT scan.
(a) (b) (c)
72
Figure 2-16 Determining appropriate viewing windows for the moving sphere on the PET images using the 4D model as a template The PET AC image for one of the scans in Series 4 windowed so that the outer edges of the moving sphere on the PET image visually matches the 4D model. (a) is the transverse view of the phantom through the centre of the moving sphere, with (b) is the sagittal view and (c) the coronal view. The 4D model was loaded onto the image to match the known position of the sphere relative to the main tank of the phantom.
(a) (b) (c)
2.2.2.5 Evaluation of the moving sphere on the CT and PET images
To compare the imaged moving sphere on the free-breathing CT scans with its
known range of motion and 4D volume:
• The imaged moving sphere on each of the CT scans in each of the “free
breathing” CT series of the phantom were contoured.
• The outer dimensions of the sphere only were contoured on each scan.
• The volumes of each of these 3D contours were then computed to
compare with the volume of the calculated 4D volume of the outer
dimensions of the moving sphere.
To compare the imaged moving sphere on the PET AC images (each scan in series 4,
6, 7 and 8) with its known range of motion and 4D volume:
• The window widths and levels previously determined for appropriately
viewing the sphere were applied to each image.
• Each of the images was viewed with the 4D model contour displayed so
that a visual comparison of the imaged sphere and the model could be
made.
73
2.2.3 Data analysis
To evaluate the baseline TPS contoured volumes on the CT scans of the phantom:
Comparison of the baseline CT contours and PET AC contours of the phantom
• The percentage differences between the calculated volumes and the
volumes of the 3D contours and the 3D model generated contours were
determined for
The inner dimension of each central rod
The inner and outer dimensions of the static sphere
The inner and outer dimensions of the 4D volume of the moving
sphere
To evaluate the contouring results on the PET AC scans of the phantom using the 3D
models created from contours on the CT images of the phantom:
• The mean volume of the contours created by loading the 3D models of the
different components of the phantom were calculated for
The inner dimension of each central rod
The inner and outer dimensions of the static sphere
The inner and outer dimensions of the 4D volume of the moving
sphere
• The percentage differences between the calculated volumes and the
means volumes of the 3D model generated contours were then determined
for
The inner dimension of each central rod
The inner and outer dimensions of the static sphere
The inner and outer dimensions of the 4D volume of the moving
sphere
Using the data from the 3D model generated contours on each PET scan, ratios of the
maximum pixel value to the maximum pixel value in the 3.0 cm rod was calculated
for each rod and the sphere. The average ratio for each rod and the sphere were
calculated for each series of PET scans. This was repeated using the maximum SUV
values extracted from the contours.
Evaluation of the pixel and SUV data obtained from the TPS
74
Line plots were then created to evaluate the pixel and SUV data obtained from the
contours created on each AC image for the different PET scan series of the phantom.
Plots of the following image data were made:
• The maximum pixel value for the central rods and static or moving sphere
for each of the 6 scans in each series (a) – (f) (refer to Table 2-4)
• The mean pixel value for the central rods and static or moving sphere for
each scan (a) – (f)
• The maximum SUV value for the central rods and static or moving sphere
for each scan (a) – (f)
• The mean SUV value for the central rods and static or moving sphere for
each scan (a) – (f)
To validate the image data in the TPS, ratios of the pixel and SUV values of the main
tank to those of each different phantom component from series 5 -8 were determined.
This provided a background percentage value (or background-activity ratio) for each
of the rods and the sphere for each image. A mean background percentage was
determined for each of the phantom components for the different series. Line plots
of the series mean background–activity ratios based on the maximum and mean
values for both the pixel and SUV data were compared a plot of the known injected
percentage background activity in the main tank.
Frequency plots were made of the window width and level values that were
determined as those that best showed the different features of the PET images
according to the chosen criteria. Five different plots were made to demonstrate the
windowing levels required for the different series of PET scans to obtain an accurate
visual representation of:
Frequency plots of the window widths and level
• The main tank on the transmission scans.
• The static or moving sphere on the transmission scans.
• The background and the central rods on the AC scans.
• The central rods only with the background windowed out on the AC
scans.
• The static or moving sphere on the AC scans.
75
To evaluate whether the imaged moving sphere on the CT scans matches its known
4D volume:
Evaluation of the moving sphere on the CT and PET images of the phantom
• The mean volume was computed from contours of the imaged moving
spheres for each free breathing series of scans.
• The distance from the known central position of the moving sphere to the
most superior and inferior aspects of the imaged moving sphere were
measured on each scan. From these measurements the following was
determined:
The total imaged length of the moving sphere in the superior to
inferior direction.
The midpoint of the imaged sphere in the superior to inferior
direction.
• The mean distance of the most superior aspect of the imaged moving
sphere was calculated.
• The mean distance of the most inferior aspect of the imaged moving
sphere was calculated.
• The mean distance of the imaged sphere’s midpoint from its known
central position was calculated.
76
2.2.4 Results
2.2.4.1 Phantom CT scan acquisition
The static series of CT scans of the phantom were acquired and loaded into the TPS
system. Transverse, sagittal and coronal views of each of the scans in this series are
shown in Figure 2-17.
Static CT scanning of the phantom
Figure 2-17 Images of the series of static CT scans of the phantom (a) = Static CT scan 1, (b) = Static CT scan 2, (c) = Static CT scan 3
(a) – main tank empty with no sphere
(b) – main tank empty with static sphere
(c) – main tank full with no sphere
77
The series of “free-breathing” CT scans of the phantom were acquired and loaded
into the TPS system. Transverse, sagittal and coronal views of each of the scans in
series 1 and 2 are shown in Figure 2-18 and series 3 and 4 in Figure 2-19.
“Free breathing” CT scans of the phantom
Figure 2-18 Images of series 1 and 2 of the “free-breathing” CT scans of the phantom Transverse, sagittal and coronal views of each scan in series 1 and 2 are shown from left to right. (a) - (e) are scans a-e for series 1 (main tank empty, sphere moving at 15 cycles/min). (f) - (j) are scans a-e for series 2 (main tank full, sphere moving at 15 cycles/min).
(a) (f)
(b) (g)
(c) (h)
(d) (i)
(e) (j)
78
Figure 2-19 Images of series 3 and 4 of the “free-breathing” CT scans of the phantom Transverse, sagittal and coronal views of each scan in series 3 and 4 are shown from left to right. (a) - (e) are scans a-e for series 3 (main tank empty, sphere moving at 20 cycles/min). (f) - (j) are scans a-e for series 4 (main tank full, sphere moving at 20 cycles/min).
(a) (f)
(b) (g)
(c) (h)
(d) (i)
(e) (j)
On visual inspection all of the “free-breathing” scans demonstrate that the moving
sphere has been imaged differently, particularly in the sagittal and coronal views of
the sphere in Figures 2.20 and 2.21. These image planes visualise the superior and
inferior orientation of the phantom, which also corresponds to both the direction of
motion of the sphere and the couch movement through the CT scanner. The
transverse views are of the CT slice corresponding to the most superior imaged
aspect of the moving sphere. The shape and dimensions of the sphere on these slices
79
is varied, particularly the spiral shapes seen in some of the images. It is expected
that the sphere in these slices would be circular and the same dimensions if the
sphere had been imaged completely along its path of trajectory.
2.2.4.2 Phantom PET scan acquisition
2.2.4.2.1 Preliminary phantom test PET scans
The first test PET scan of the phantom used a solution of 30 MBq of 18
F-FDG in
11000 ml of water (0.0027 MBq/ml) in the main tank of the phantom with no activity
in the central rods (see Figure 2-20). The second test scan with 1 MBq in 254 ml of
water in central rods, a similar activity concentration (0.0039 MBq/ml) to the first
scan, failed to image the 0.5 cm and 1.0 cm central rods, indicating that this level of
activity per ml was too low for imaging the different components of the phantom.
Figure 2-20 The first test PET scan 30 MBq in 11000 ml of water in the main tank of the phantom (0.0027 Mbq/ml) with no water or activity in the central rods.
(a) (b)
The last two test scans used a concentration of 0.0197 MBq/ml in the central rods,
which equates to 5 MBq in 254 ml of water. One of these scans had this activity in
the central rods only (Figures 2-21a and 2-21b) and the other with 5 MBq in 254 ml
of water in the central rods and 12.3 MBq in 12500 ml of water in the main tank,
resulting in the main tank activity being 5% of the activity in the central rods
80
(Figures 2-21c and 2-21d). All of the central rods were clearly imaged in both of
these scans.
Figure 2-21 Test PET scans with 0.0197 MBq/ml concentration of 18-FDG Each of the images has a concentration of 0.0197 MBq/ml of a water-18-FDG solution in the central rods. Images (a) and (b) are transverse and sagittal views of the scan with only the rods filled and images (c) and (d) are transverse views of the scan with the main tank activity being 5% of the activity in the central rods.
(a) (b) (c) (d)
The solution of 20 ml of saline and 2 MBq of 18
F-FDG (0.1 MBq/ml) used to inject
the MM3003 fiducial markers was sufficient for the markers to be clearly imaged on
the PET AC images of the phantom (see Figure 2-22)
Figure 2-22 Appearance of the fiducial markers on the PET AC images
81
2.2.4.2.2 Different phantom PET scan conditions for use in image registration
and GTV delineation protocol testing
PET scans were acquired of the phantom to simulate different “patient specific”
parameters relating to injected activity, background uptake and motion of a lesion
due to respiration, resulting in 8 different series of images. Figures 2-23 and 2-24
show AC and transmission scan images from scan (a) of each of these series. 6 scans
(a-f) were acquired for each of the different series, however due to reconstruction
errors only 5 scans were acquired for series 8. The images for series 8 all
demonstrate a slight warping on the sagittal and coronal planes. A reconstruction
error also occurred during the acquisition of the scans for series 4. The left fiducial
marker in the scans from series 2 was not seen on any of the AC images for this
series (see Figure 2-23). Neither the left nor right fiducial markers were imaged on
any of the AC images from the scans for series 6 (see Figure 2-23).
82
Figure 2-23 AC emission scan images from the different phantom PET scan series
Trans AC Sag central rods AC Sag sphere AC
Series 1
Series 2
Series 3
Series 4
Series 5
Series 6
Series 7
Series 8
83
Figure 2-24 Transmission scan images from the different phantom PET scan series
Trans AC Sag central rods AC Sag sphere AC
Series 1
Series 2
Series 3
Series 4
Series 5
Series 6
Series 7
Series 8
84
2.2.4.3 Image quantification using the treatment planning system tools
There were differences between the volumes of the calculated, the 3D threshold-
based contours and the 3D model-generated contours for the central rods and the
static lesion (see Table 2-5).
Baseline contouring of the CT images of the phantom
Table 2-5 Volumes of the components of the phantom on CT – calculated and TPS generated volumes using different contouring methods
Component dimension
Calculated
volume
(cm3
3D threshold-based
contour
)
3D model generated
contour
Volume
(cm3
% diff to
calculations )
Volume
(cm3
% diff to
calculations )
Central rods 0.5 cm 2.3 2.0 -13 4.7 +104.3
1.0 cm 11.6 12.8 +10.3 15.7 +35.3
1.5 cm 32.3 31.9 -1.2 33.8 +4.6
2.0 cm 51.9 50.7 -2.3 50.1 -3.5
3.0 cm 120.9 122.9 +1.7 131.7 +8.9
Sphere Static – inside 20.6 20.3 -1.5 20.3 -1.5
Static – outside 47.7 47.1 -2.3 47.1 -1.3
The magnitude of difference between the calculated and the TPS generated volumes
on the CT image is considerably higher for the two features of the phantom with the
smallest diameters, the 0.5 cm and 1.0 cm central rods. The magnitude of the
percentage difference between the calculated and the 3D threshold-based contour
volumes for the features of the phantom with a diameter >1 cm are similar, with their
mean percentage difference equal to 1.6 ± 0.4%. The magnitude of the percentage
difference between the calculated volumes and the volumes of the 3D model
generated contours for the features of the phantom with a diameter >1 cm was more
varied, with their mean percentage difference equal to 4.0 ± 3.1%.
85
A 4D volume to provide a visual representation of the moving sphere along its path
of trajectory was generated via two different contouring techniques. There were
differences between the calculated and TPS generated volumes for the 4D volume of
the moving sphere (see Table 2-6). There was no difference in the volumes of the
4D moving sphere generated by either adding the contours of the 5 differently
positioned static scans of the sphere together or the contour generated using a 3D
model. The percentage difference between the calculated and both of the TPS
contouring methods for the inner and outer dimensions of the 4D moving sphere
were 4.4% and 3.9% respectively.
Creation of a model of the 4D moving sphere
Table 2-6 Volume of the 4D moving sphere on CT – calculated and TPS generated volumes using different contouring methods
Dimension
Volume (cm3
Calculated
)
3D threshold-based contour
3D model generated contours
4D model – inside 38.8 40.5 40.5 4D model – outside 79.6 82.7 82.7
Contours of the central rods, the static sphere and the 4D volume of the moving
sphere were generated using the 3D models of these features for each of the AC
images in series 1-8 of the PET scans of the phantom. The mean volume of each
individual contoured compartment (derived from the volumes calculated by the TPS
ROI tools) generated on the images within each series are shown in Table 2-7. The
mean volume for each of the contoured rods and spheres was derived from the
contours generated on every PET AC image is also shown.
Contouring of the PET AC images of the phantom
86
Table 2-7 Intra-series mean volumes and the overall mean volumes of the 3D model-generated contours of the phantom components on the PET AC images
Component dimension
Mean volume (cm3
PET scan series
) of the 3D model-generated contours Overall Mean ±
SD 1 2 3 4 5 6 7 8
Central rods
0.5 cm 7.3 7.7 7.2 6.0 8.5 5.8 7.5 7.8 7.2 ± 0.9
1.0 cm 16.6 21.9 16.5 22.5 14.4 21.8 16.5 16.1 18.3 ± 3.2
1.5 cm 34.5 40.9 34.8 40.2 35.6 42.4 37.4 34.6 37.6 ± 3.2
2.0 cm 61.4 62.9 61 62.7 61.7 62.2 61.4 62 61.9 ± 0.7
3.0 cm 133.6 128.6 136.2 129.7 138.3 136.9 138.8 133.5 134.5 ± 3.8
Sphere Static inside - - 21 - - - - - 21.0
Static outside - - - 45.5 - 45.1 45.3 45.4 45.3 ± 0.2
The overall mean volumes of the contours generated from the 3D models of the
central rods and sphere from all of the PET AC images are different to the calculated
volumes. The percentage differences of the calculated and the average 3D model
generated volumes for each component of the phantom for both CT and the PET AC
images are shown in Table 2-8. It can be seen that the 0.5 cm and 1.0 cm central rod
volumes for the 3D model generated contours are significantly higher than the
calculated volumes for both the CT and PET images. For the PET images, the
magnitude of the percentage difference between the calculated volumes and the 3D
model generated volumes of the central rods with a diameter >1 cm are similar, with
their mean percentage difference equal to 15.6 ± 4.1%. The percentage differences
for the volumes of the static and moving sphere are similar on both CT and PET.
Table 2-8 Percentage difference of the 3D model-generated volumes for both the CT and PET images to the calculated volumes of the phantom components
Component dimension Calculated
volume (cm3
3D model volume (cm
)
3
CT
) after conversion to contours
PET
Volume (cm3
% diff to calculations )
Mean volume (cm3
% diff to calculations )
Central rods
0.5 cm 2.3 4.7 104.3 7.2 214.1 1.0 cm 11.6 15.7 35.3 18.3 57.7 1.5 cm 32.3 33.8 4.6 37.6 16.3 2.0 cm 51.9 50.1 3.5 61.9 19.3 3.0 cm 120.9 131.7 8.9 134.5 11.2
Sphere Static – inside 20.6 20.3 1.5 21.0 1.9 4D model – inside 38.8 40.5 4.4 45.3 5.0
87
The average ratios for each PET series of the phantom of the maximum pixel value
in each rod or the sphere to the maximum pixel value of the 3.0 cm rod were not
equal to 1.0 (see Table 2-9). It should be noted that except for series 2, the same
concentration of activity was injected into each feature of the phantom (1MBq was
injected into each rod for series 2). The ratios calculated using the maximum SUV
values were identical to those calculated using the maximum pixel values.
Quantification and validation of PET pixel and SUV data in the TPS
Table 2-9 Average ratios of the maximum pixel value in each rod or the sphere to the 3.0 cm rod maximum pixel value
Component dimension Ratio PET scan series
1 2 3 4 5 6 7 8
Central rods
0.5 cm 0.21 7.13 0.23 0.2 0.16 0.16 0.16 0.23
1.0 cm 0.66 5.53 0.63 0.65 0.48 0.41 0.37 0.35
1.5 cm 0.97 4.4 1.02 0.90 0.70 0.68 0.62 0.56
2.0 cm 0.88 2.18 0.93 0.94 0.89 0.92 0.81 0.74
3.0 cm 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Sphere Static - - 0.64 - - - -
Moving - - - 0.69 - 0.61 0.56 0.49
Plots (see Appendix 1) of the maximum and mean pixel and SUV values for the
central rods and the sphere (static or moving) obtained from the TPS contours
demonstrated:
• Decay characteristics of radioactive substances when the order of the
scans acquired for each series is taken into account. This observation is
based on the plots of the pixel values.
• SUV values are a normalised value relative to total injected activity and
weight. This observation is based on the trendlines for each feature of the
phantom for:
Series 1 – 4 following the same trend as seen with the pixel value
plots (there was no background activity for these images)
Series 5 – 8 having a gradient = 0 and the SUV values
approximately halving as the background activity doubles.
88
• The pixel and SUV values in the smaller diameter rods decreased below
that of main tank as the percentage of activity in the main tank increased,
despite the central rods always having a higher concentration per ml than
the main tank.
• The range of the maximum pixel values of the different rods were similar
for
Series 1, 3 and 4 (no water or activity in the main tank), and scans
a b, and f only for series 4. The values for scans c, d, and e are
considerably higher than those observed for the other three scans
in series 4.
Series 5 – 8 (water and activity in the main tank).
• There were differences in the relationship between the plots of the
individual compartments of the phantom for maximum and mean pixel
and SUV data.
Line plots of the background-activity ratios of the main tank to the central rod or
sphere of the PET AC image data for series 5 - 8, demonstrated that only the 3 cm
diameter rod followed the known background percentage plot line for only the
maximum pixel and SUV data (see Figures 2-25a and 2-25b). The moving sphere
background-activity ratios are slightly higher than those of the 1.5 cm rod. This
agrees with the trends displayed in the plots of the maximum pixel and SUV values.
The plots of the background-activity ratio values for the mean pixel and SUV data
demonstrate a larger background-activity ratio than the known concentration of
activity in the water in the main tank compared to that in the central rods (note the 3
cm rod) and the moving sphere (see Figures 2-25c and 2-25d)
89
Figure 2-25 Plots of the background-activity ratios of the main tank pixel data to the sphere and central rod pixel data for the PET AC images: Series 5-8 with conditions as per Table 2-4. (a)=Ratios based on maximum pixel values, (b)=Ratios based on mean pixel values, (c)= Ratios based on maximum SUV values, (d)= Ratios based on mean SUV values.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Series 5 Series 6 Series 7 Series 8Series
Bkg
max
pix
el n
umbe
r/ V
OI m
ax
pixe
l num
ber
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Series 5 Series 6 Series 7 Series 8Series
Bkg
max
pix
el n
umbe
r/ VO
I max
pix
el
num
ber
(a) (b)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Series 5 Series 6 Series 7 Series 8
Series
Bkg
max
SU
V nu
mbe
r/ VO
I max
SU
V nu
mbe
r
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Series 5 Series 6 Series 7 Series 8
Series
Bkg
mea
n SU
V nu
mbe
r/ V
OI m
ean
SUV
num
ber
(c) (d)
90
2.2.4.4 Determination of window widths and levels for PET images
Optimal viewing window widths and levels for the PET were determined where a
geometrical visual match of different features of the phantom on both the PET AC
and transmission images and its registered CT scan was achieved. Frequency plots
(see Figures 2-26 and 2-27) demonstrated:
• The same window width (WW) and window level (WL) could be applied
to each transmission scan from all the different PET series.
• Varying viewing windows were required for the AC images and these
were dependant on the features being specifically viewed and the
percentage of activity concentration in the main tank relative to the
central rods (i.e. the background percentage).
For background and the central rod visibility a WW = 0.2 and WL
= 0 could be applied for all the images except for the series 8
images.
To view the central rods only, the same WW used for viewing
both the background and the rods with a WL of 0.5 or 0.1 was
found to provide a geometrical visual match of the inner
dimensions of the central rods on the CT images.
To view the full range of motion of the moving sphere, a WW of
0.1 or 0.05 and a WL = 0 was required.
Figure 2-26 Frequency plots of window widths and levels for viewing different features on the PET transmission scans of the phantom The transmission scans were windowed to visually match the geometry of the main tank of the phantom on the registered CT images with PET images (series 1-8)
Phantom PET trans scan window width and levels Series distribution for central rod and background visibility
0
1
2
3
4
5
6
7
8
WW 0.05WL 0.0
WW 0.05WL 0.1
WW 0.1WL 0.0
WW 0.1WL 0.05
WW 0.2WL 0.0
WW 0.2WL 0.05
WW 0.2WL 0.1
WW 0.4WL 0.2
WWL value ranges
Num
ber o
f ser
ies
with
val
ue
rang
es
Series 8
Series 7
Series 6
Series 5
Series 4
Series 3
Series 2
Series 1
91
Figure 2-27 Frequency plots of window widths and levels for viewing different features on the PET AC scans of the phantom (a) = Windowing parameters for the PET AC scans (from series 1-8) where the background and central rods visually matched the geometry of these features on the registered CT images. (b) = Windowing parameters for the PET AC scans (from series 1-8) where the central rods visually matched the geometry of these features on the registered CT images). (c) = Windowing parameters for the PET AC scans to visually match the geometry of the sphere on either the registered CT images (static sphere-series 3) or the 4D model (moving sphere-series 4 and 6-8)
Phantom PET AC scan window width and levels Series distribution for central rod and background visibility
0
1
2
3
4
5
6
7
8
WW 0.5WL 0.0
WW 0.1WL 0.0
WW 0.15WL 0.0
WW 0.2WL 0.0
WW 0.2WL 0.5
WW 0.2WL 0.1
WW 0.3WL 0.0
WW 0.3WL 0.1
WWL value ranges
Num
ber o
f ser
ies
with
val
ue
rang
es
Series 8
Series 7
Series 6
Series 5
Series 4
Series 3
Series 2
Series 1
(a)
Phantom PET AC scan window width and levels Series distribution for central rods only visibility
0
1
2
3
4
5
6
7
8
WW 0.5WL 0.0
WW 0.1WL 0.0
WW 0.15WL 0.0
WW 0.2WL 0.0
WW 0.2WL 0.5
WW 0.2WL 0.1
WW 0.3WL 0.0
WW 0.3WL 0.1
WWL value ranges
Num
ber o
f ser
ies
with
val
ue
rang
es
Series 8
Series 7
Series 6
Series 5
Series 4
Series 3
Series 2
Series 1
(b)
Phantom PET AC scan window width and levels Series distribution for spherical lesion visibly matching models
0
1
2
3
4
5
6
7
8
WW 0.5WL 0.0
WW 0.1WL 0.0
WW 0.15WL 0.0
WW 0.2WL 0.0
WW 0.2WL 0.5
WW 0.2WL 0.1
WW 0.3WL 0.0
WW 0.3WL 0.1
WWL value ranges
Num
ber o
f ser
ies
with
val
ue
rang
es
Series 8
Series 7
Series 6
Series 4
Series 3
(c)
% activity concentration of the main tank relative to the central rods Series 8 = 40% Series 7 = 20% Series 6 = 10% Series 5 = 5% Series 1-4 = 0%
The sphere was surrounded by air for every scan resulting in a 0% background activity.
92
2.2.4.5 Evaluation of the imaged moving sphere on the CT and PET images
The contoured outer dimensions of the imaged moving sphere on the free-breathing
CT scans of the phantom used for comparison with the sphere’s known range of
motion and 4D volumes are shown in Figure 2-28.
Figure 2-28 Overlaid 3D contours of the moving sphere imaged for each scan from the different free breathing series of CT scans Transverse, sagittal and coronal images (from left to right) of the moving sphere are shown for each scan.
Main tank empty, sphere moving at 15 cycles/min
Main tank full, sphere moving at 15 cycles/min
Main tank empty, sphere moving at 20 cycles/min
Main tank full, sphere moving at 20 cycles/min
93
The volumes of each of these 3D contours are shown in Table 2-10. None of the
contours of the imaged moving sphere match the calculated 4D volume of the sphere
(outer dimension) of 79.6 cm3. The volumes range from 20.9 cm3 to 61 cm3 with a
mean of 40.4 ± 15.5 cm3
.
Table 2-10 Imaged volumes of the moving spherical lesion from the different free breathing series of CT scans
Series Volumes (cm3
Scan 1
) contoured to the outside dimension of the spherical lesion
Scan 2 Scan 3 Scan 4 Scan 5 Series Mean
1
(15 cycles/min Main tank empty)
60.0 37.3 45.5 21.8 23.3 37.6
2 (20 cycles/min
Main tank empty) 23.9 52.8 21.6 45.2 22.3 33.2
3 (15 cycles/min Main tank full)
42.3 59.4 30.2 25.7 61.0 43.7
4 (20 cycles/min Main tank full)
59 32.5 25.7 59.3 20.9 39.5
Figure 2-29 demonstrates the differences in the imaged superior and inferior aspects
of the moving sphere for each of the free breathing CT scans. The vertical lines of
the graph demonstrate the measured imaged length of the moving sphere in the
superior and inferior directions for each scan, from the central position of the sphere
along its plane of motion. The maximum distance between the most superior and
inferior aspects of the sphere was 65 mm along its path of motion (in the superior to
inferior directions). None of the CT images imaged the sphere’s full range of
motion. The mean centre of the imaged sphere (½ the imaged length) derived using
the data from all 20 free breathing CT scans, was found to be 2 mm inferior of the
true centre of the moving sphere. The mean imaged superior extent of the moving
sphere was 18.5 mm and the mean imaged inferior extent was 21 mm.
94
Figure 2-29 Graphical representation of the variation in the imaged superior and inferior aspect of the moving sphere from the free breathing CT scans Actual superior and inferior extents of moving sphere Mean centre of the imaged sphere (1/2 imaged length) Mean imaged superior extent of the sphere Mean imaged inferior extent of the sphere
-35
-30
-25
-20
-15
-10
-5
0
5
10
15
20
25
30
35
1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
Imag
ed le
ngth
of m
ovin
g sp
here
(mm
)
Series 2 Series 3 Series 4Series 1
Supe
rior d
irect
ion
Infe
rior d
irect
ion
Images used for the visual comparisons of the imaged moving sphere on the PET AC
scans of the phantom with the sphere’s known range of motion and 4D volume are
shown in Figures 2-30 and 2-31. The moving sphere is shown to match the 4D
model (orange contours) for series 4 and 6, though scans (c) and (d) from series 4
appear to have slightly different dimensions and shape to the other images in this
series. However the images of the moving sphere for series 7 and 8 do not
completely match the contours of the 4D model. The sagittal images for series 8
show the moving sphere to be angled differently from its plane of motion.
.
95
Figure 2-30 Visual comparison of moving sphere imaged on PET AC scans with the 4D model: Series 4 and 6. Transverse, sagittal and coronal images (from left to right) of the moving sphere are shown for each scan.
Scan 4a Scan 6a
Scan 4b Scan 6b
Scan 4c Scan 6c
Scan 4d Scan 6d
Scan 4e Scan 6e
Scan 4f Scan 6f
96
Figure 2-31 Visual comparison of moving sphere imaged on PET AC scans with the 4D model: Series 7 and 8 Transverse, sagittal and coronal images (from left to right) of the moving sphere are shown for each scan.
Scan 7a Scan 8a
Scan 7b Scan 8b
Scan 7c Scan 8c
Scan 7d Scan 8d
Scan 7e Scan 8e
Scan 7f
97
2.2.5 Discussion and conclusions
The levels of accuracy of PET-based contours were assessed using the baseline
contours performed on the CT scans. The PET-based contours were found to have a
higher percentage error than the CT-based contours, despite using 3D models to
derive the contours. The image quantification results demonstrate that the standard
whole body PET scanning protocols provide image data which can be extracted by
the TPS tools, with the maximum pixel and SUV data proving more consistent
indicators of uptake than the mean values. Window widths and levels which provide
a geometrically accurate visualisation of different features of the phantom on the
PET images were dependant on background activity and motion. Evaluation of the
moving sphere on both the CT and PET images acquired under “free-breathing”
conditions demonstrate that PET images of a moving object may be able to provide a
4D volume of tumours under the influence of respiration.
CT and PET images of the phantom were acquired with fiducial markers using
standard planning CT and whole-body PET clinical acquisition protocols. Various
patient conditions such as respiration and increasing normal tissue (or background)
uptake were simulated. The activity levels used for acquisition of the PET images
were similar to those in other phantom studies59, 102, 109. The fiducial markers were
easily applied to the CT images but their use was not so straight forward for the PET
images. It took a number of attempts to successfully inject the small amounts of the 18F-FDG and saline solution, so that it was contained within the marker. Despite
only a few drops of 18F-FDG and saline solution being injected into a 5 mm diameter
space the markers visually look larger than this on the PET images. The PET imaged
marker size and the extra time it took to inject the markers needs to be considered in
terms of efficacy of use. While the injection procedure became quicker with practice
and a shielded syringe was used for injecting the markers, it would be expected that
the use of fiducial markers would add to the accumulative annual radiation exposure
for nuclear medicine technologists. Injection times can significantly contribute to
hand exposure due to the emitted positrons from the 18F-FDG110
.
Care must be taken to discriminate between different DICOM formats when the
image data is exported from the PET scanner to DVD. There are two DICOM image
98
export formats available in most scanners. One is a 3D image data DICOM format
with the second format consisting of the separate images of the each slice of the scan
accompanied by DICOM viewing software. The second format is most commonly
provided for referring doctors instead of hardcopy films by the nuclear medicine
technologists. The PET AC and transmission scans were successfully imported into
the TPS using the 3D image DICOM format. Due to the different table feed and scan
acquisition direction between PET and CT, all of the PET images needed to be
flipped as they were imported in the TPS as they were upside down with respect to
the planning CT scans. Flipping these images could be successfully performed
without errors with respect to orientation or distortion of the images.
The CT and PET phantom images of the phantom demonstrate image attributes
described in the literature. The PET images had a much lower spatial resolution that
the CT images as expected50, 60. The voxel sizes of the CT images were 0.15 x 0.15 x
0.30 cm3 or 0.13 x 0.13 x 0.50 cm3 depending on the slice thickness selected when
the scans were acquired. (The variation in slice thickness for the different CT scans
was not intentional; it was due to changes in the set-up of scanning protocols on the
CT scanner). The voxel size of the PET emission and transmission images was 0.4 x
0.4 x 0.4 cm3
.
The level of errors (see Table 2-8) in the volumes of the 3D model-based contours
for both the CT and PET images were related to the way in which the contours were
generated and image voxel size. It is important to note that when the 3D models
were loaded onto both the CT and PET images of the phantom, they were not
adapted. However when the models were converted to contours, this conversion
process appeared to make the contours encompass whole voxels. This explains why
the magnitude of the percentage differences of the volumes for the 3D model-based
contours on the PET images, are larger than those on the CT images. The magnitude
of error for contoured volumes was significantly reduced for objects >1 cm diameter
(i.e. there are more voxels across the diameter of the objects). This effect of voxel
size was seen in Pekar et al’s study using model-based segmentation tools100
.
The contrast resolution of the PET images decreased as the background activity in
the main tank of the phantom increased. The features of the phantom with smaller
99
diameters (the 0.5 cm and 1.0 cm diameter rods) could not be seen as distinct objects
with main tank background activities of 20% and 40%. The ratios of the different
central rod and sphere maximum pixel values to the maximum pixel value of the 3.0
cm rod provide a reason for this. The ratios decrease as the size of these VOIs
decrease and as the activity in the main tank increases (see Table 2-9). This is due to
the effect of object size and TBRs on detection sensitivity due to count recovery
losses, a well documented phenomena of PET imaging56, 57
.
The line plots of the pixel and SUV image data further demonstrate the effect of
object size and TBRs on count recovery losses (see Appendix 1). While SUV values
are normalised to total injected activity and weight, it is also dependant on the uptake
within VOIs. Therefore a reduction in measured activity in imaged VOIs compared
to small objects and increasing background will also affect SUV values109, 111. The
effect of object size and increasing background on SUVs within PET volumes has
implications when considering using an absolute SUV value as a threshold for
contouring. Under-estimates of true volume size would definitely occur if a
threshold > 2.5 SUV is used for contouring VOIs on PET images (as has been
suggested5). The results of several studies have demonstrated this as well as a failure
to contour small PET volumes using this threshold112, 113
.
The results of the PET image analysis demonstrated that the use of thresholds based
on either the maximum pixel or SUV values may actually be more valid. Hoffman et
al56 originally demonstrated that maximum pixel values are a true indication uptake
within a VOI on a PET image. Successive studies6, 109, 111
have demonstrated that
mean estimates of activity within a VOI are heavily influenced by partial voluming
effects and lower background activity (i.e. the ROIs used to determine the mean
value including voxels outside of the VOI). The line plots of the ratios of the
background activity to the activity in the rods and the sphere demonstrate a closer
agreement of the 3.0 cm diameter rod with the true background-activity-ratio when
the maximum values are used compared to the mean values (see Figure 2-25).
The plots of the background-activity-ratio for both the maximum pixel and SUV data
indicate that there may be a linear relationship between these two values as indicators
of imaged activity. The fact that the ratios of the maximum SUV values within the
100
different features of the phantom to the 3.0 cm rod in each image were identical to
those for the calculated using the maximum pixel values provides evidence that this
is the case. This suggests that applying the same threshold value to either the
maximum pixel or SUV value would result in identical contouring results and needs
to be examined as part of the contouring tests.
It was expected that the range of the pixel values in the central rods would be the
same for each of the series of the PET images of the phantom as the same
concentration of activity was maintained for the acquisition of the different images
(except series 2). The reason for this is most likely due to the absence of water in the
main tank of the phantom for series 1 – 4. Water in the main tank would have
attenuated more of the emitted photons from within the rods. The absence of water
in the main tank also accounts for the difference in the SUVs. The surrounding
density of objects affects the calculation of SUVs when attenuation corrections are
applied to emission scans111
.
The scans in series 1 and 2 were not all acquired on the same day which accounts for
the line plots of the pixel data for these series not progressively reducing with each
scan. However the scans in series 4 were acquired on the same day in sequential
order (i.e. scan (a) first to (g) being the last). The first scan attempted failed to
reconstruct and scans c, d, and e in the series show a dramatic jump in counts and
SUV. There also appears to be a reconstruction error in the series 8 images. On the
sagittal and coronal views (see Figure 2-23) it seems as if the couch position changed
during scan acquisition. This was not the cause as this effect is seen in all scans the
scans in this series. Due to limited access time on the PET scanner, the series 4 and
8 scans were not repeated.
Due to the variation in the pixel values across the different PET images of the
phantom, a windowing scale that was relative to the maximum image value was
more easily applied and effective in assessing appropriate viewing windows for these
images. Using the CT images of the phantom as a visual reference proved effective
in determining window widths and levels which resulted in geometrically accurate
visual representations of the different features of the phantom for the PET images.
As the PET transmission scans images are density-based, a universal viewing
101
window of WW = 40% and WL = 20% can be applied to these images. However
viewing windows for PET AC images were found to be affected by background
activity levels and the presence of motion during scan acquisition.
Despite these findings, a general PET viewing window of WW = 20% and WL = 0%
(relative to the image maximum) was suitable for displaying both the background
uptake and the central rods and could be applied to almost all the different simulated
patient conditions in the phantom images. This viewing window was not suitable for
accurately displaying the dimensions of the central rods when the background
activity was 40% or for viewing the moving sphere. A WW = 20% of the maximum
and a WL = 5 – 10% displayed the central rods without the background uptake (again
except when the background activity was 40% and for viewing the moving sphere).
This second viewing window is similar to Hong et al114
findings; narrowing the WL
to within 10 – 15% of the WW allowed the central rods to be well defined with
reasonably sharp edges.
A WW = 10% and WL = 0% was found to be suitable for viewing the moving sphere
based on visual correlation of the modelled contour of the 4D volume of the moving
sphere. This qualitative assessment demonstrated that the moving sphere was
imaged over its full range of motion on the PET images of the phantom, except when
the activity in the main tank relative to the central rods and sphere increased above
20%. The mean pixel and SUV values were considerably lower for the moving
sphere compared to those for the static sphere which was indicative of the much
lower values observed at the periphery of the imaged moving sphere (also observed
by Caldwell et al70
).
As expected the series of “free- breathing” CT scans of the phantom where the
sphere was moving did not image the true range of motion of the sphere. Chen et al7
state that the lengthening or shortening of an object moving in the direction of the
long axis of the couch (or the z-axis of the final image) is due to the phase of the
object motion relative to the imaging plane of the scanner. The spiral shaped
distortions of the sphere’s dimensions in the transverse image plane (shown in
Figures 2-18 and 2-19) are due to the motion of the sphere, the helical acquisition of
the CT data on the Siemens MSCT scanner and the interpolation between the
102
detector rows of the CT scanner115. The differences in the imaged dimensions of the
moving sphere between the PET and CT images has implications with respect to
accurately registering the images and defining a 4D PET-based target volume.70
103
2.3 Patient image acquisition and analysis
2.3.1 Aims
1. To acquire planning CT and PET images for adequate numbers of patients
undergoing radiation therapy that could be used to test image registration and
GTV delineation protocols determined from the phantom data.
2. To determine appropriate clinical protocols for acquiring patient PET scans that
are suitable for use in the radiotherapy treatment planning process. These
protocols would ensure that:
• The patient was in the same position that would be required for their CT
planning scan and radiotherapy treatment.
• Both the AC emission and transmission PET images would be able to be
imported into the TPS for registration with the planning CT scan
3. Analyse each patient’s PET images using techniques defined on the phantom
PET images to determine:
• Maximum count and SUV data in regions of high uptake that correspond
to malignant disease.
• A background (normal tissue) uptake level.
4. To evaluate the application of the PET viewing window protocols determined
from the phantom image tests, to patient images.
104
2.3.2 Methodology
2.3.2.1 Estimation of patient image numbers for protocol trials
The desired outcomes of this research project were that the protocols used as part of
the RT image registration and RO tumour volume definition trials would show high
levels of precision and reproducibility. Sufficient numbers of patient data sets were
required for the study to be able to demonstrate a clinically significant mean
difference for both the registration and contouring results. Taking into account the
uncertainty margins applied to GTVs for lung cancer at the time the project was
started, it was desirable for the protocols to demonstrate that:
• Different RTs could register CT and PET images with a 5 mm mean
difference from a baseline registration.
• Different ROs could define a volume on a PET scan registered with
planning CT scan with a 5 mm mean difference in any direction (superior,
inferior, right, left, anterior and posterior).
The required mean levels of difference and the limitation on the number of available
ROs for participation in the contouring trials were factors when an estimate of the
number of patient images required for the protocols trials was made. It was
determined that:
• 20 patient PET and planning CT scans would need to be acquired
• 6 RTs would be required to register CT and PET images
• 6 ROs would be required to define the tumour volumes.
• These numbers of patient image data sets and participants would result in:
A mean difference of 5 mm demonstrated between the
participants’ results for each patient with α = 0.05
A 95% probability of detecting a true difference between the
image registration and the contouring methods.
2.3.2.2 Clinical protocols for acquiring patient PET scans
Only PET scans where the patient was scanned in the same position required for their
planning CT scan were to be used for the image registration and GTV delineation
105
trials. Both the AC emission and transmission PET images for these patients would
be required as part of the image registration trials.
A process for identifying patients that were possible candidates for radiotherapy
needed to be established. This was achieved by attending the combined oncology
clinic where lung cancer patients would present for their treatment management
options (surgery, chemotherapy and/or radiotherapy). Initial inquiries highlighted
the fact that a PET scan was usually requested at the time of the patient’s initial
presentation at this clinic to verify the stage of their disease.
A system was set up to flag potential radiotherapy patients to the nuclear medicine
technologists performing the PET scans. A sticker was attached to the PET request
form highlighting that the patient was being considered for radiotherapy along with a
request for the AC emission and transmission scans to be burnt on CD in the correct
DICOM format (the PET transmission scans were not routinely archived once the
AC emission scan was reconstructed). The nuclear medicine technologists were
contacted and the patient positioning requirements were discussed. A flat couch top
and the arm stabilisation device and a head and neck rest was on site at the scanner.
A standard patient position on the couch using the radiotherapy stabilisation devices
was used for the identified patients. Assistance with the patient radiotherapy
positioning requirements was provided onsite at the PET scanner.
2.3.2.3 PET scan acquisition
The routine scanning procedures for staging lung cancer in use in the nuclear
medicine department throughout the duration of this project were used for the PET
scans of each identified patient as per ethics approval. Additionally, the PET scans
of the phantom were based on these routine scanning procedures including selecting
the same scanning and image reconstruction protocols. These scans were able to be
successfully imported, manipulated and quantified in the TPS.
All patient PET scans were acquired on a Philips Allegro stand-alone PET scanner
using the following procedures:
106
• The patient was weighed, with their weight recorded and entered as part
of the scan acquisition and reconstruction parameters so that SUV data
was available on the AC emission image.
• The patient was injected with a solution of saline and 18F-FDG one hour
prior to scan acquisition. Injected activity was varied for each patient to
account for their weight and source decay to ensure adequate
concentration and accumulation of 18
• Patient position:
F-FDG during the scan.
Supine on flat couch top using the radiotherapy stabilisation
equipment in their intended CT planning and treatment position.
• Entered scan orientation parameters:
Supine, feet first, table direction moved out, patient scanned from
head to feet
• Scan length:
The level of the external auditory meatus (EAM) to the level of
the patient’s mid thigh region
• FOV = 576 mm
• Frame acquisition:
Number of frames dependant on patient height
Emission scan frames: scan time = 3 mins/frame
Transmission scan frames: scan time = 1.54 mins/frame
• Image reconstruction
144 x 144 mm image matrix size
Reconstruction algorithm = body/RAMLA 3D/3D AC/SUV
No fiducial markers were used in any of the patient PET scans due to concerns that
the activity in the markers could be mistaken for metastatic disease (see ethical
considerations in Chapter 1).
2.3.2.4 CT scan acquisition
The routine planning CT scanning procedures for lung cancer in use at the
radiotherapy department throughout the duration of this project were used for
107
scanning the patients who did proceed to commence radiotherapy after their staging
PET scan as per ethics approval.
All the patient planning CT scans were acquired on a Siemens Somatom “Open
Sensation” 20 slice, wide bore CT scanner using the following procedures:
• Patient position:
Supine on a flat couch top using the radiotherapy stabilisation
equipment in their intended CT planning and treatment position.
• CT ball bearing fiducial markers were placed on the patient’s anterior
skin surface on midline at the superior-inferior level of their known
primary disease.
• The scanner lasers were aligned with the fiducial markers and the couch
was zeroed at the alignment point of the lasers and the markers
• Entered scan orientation parameters:
Supine
Head first
Table moves into scanner
• Scan length:
From the inferior aspect of the chin to 5 cm below the inferior
aspect of both lungs.
• 3 mm slice thickness
• FOV = 750 mm
• The standard chest protocol scanning parameters was selected:
70 mAs
120 kVp
Pitch = 1.2
• Image reconstruction
512 x 512 mm matrix size
Siemens adaptive multiplanar reconstruction (AMPR) algorithm
• The patients were instructed to breathe normally throughout the scan.
The “free breathing” CT scans of the phantom were based on the scanning
procedures detailed above.
108
2.3.2.5 Image analysis
Each of the patient PET and CT images were imported and registered in the
Pinnacle3 TPS. The method of image quantification developed on the phantom PET
scans was applied to patient PET AC images to analyse the background-activity
ratios on the patient data. To extract the maximum pixel or SUV values from the
patient images a 3D model of a sphere (already existing in the MBS tools in
Pinnacle3
) was loaded and positioned over the various volumes and regions of
interest within the patient (see Figure 2-32).
Figure 2-32 Method for obtaining image data from the PET AC patient images The images demonstrate the 3D model generated contours used to obtain image data from the tumour (yellow contour), normal lung tissue (purple contour) and the liver (blue). The images shown are different orientations at the level of the primary tumour. The orientations of the images are: the top left image is a transverse, the bottom left image is sagittal and the image on the right is coronal.
This model was expanded and rotated to cover the intense uptake regions
corresponding to malignant lung cancer identified on the radiologist’s report for each
109
patient’s PET scan. The model was then converted into a 3D contour so that the TPS
ROI tools could be used to obtain the maximum pixel and SUV data for the primary
tumour and nodes of each patient. Background (normal tissue) uptake in both the
lung and liver was determined by loading the 3D model of a sphere onto the liver and
disease free regions of the ipsilateral lung on the PET images. Lung-based and liver-
based background-activity ratios relative to the GTV or involved lymph nodes were
calculated for each patient’s PET AC image.
2.3.2.6 Application of phantom-based image windowing results
The image windowing results for viewing PET images that had been determined
using the phantom PET images were tested on the patient PET images (refer to
Figures 2-26 and 2-27). The lung or liver background-activity ratios for the patient
PET AC images were equated with the known percentage of activity in the main tank
of the phantom used in the different PET scan series. The patient background-
activity ratios were extrapolated to the nearest background percentages of the
phantom PET AC images to determine the viewing windows that should be applied
for each patient image.
Different window widths and levels were applied to demonstrate various features on
the AC scans based on the type of tissue surrounding the primary or nodes for each
patient. If a primary tumour or any involved nodes were surrounded predominantly
by lung tissue then the lung-based background-activity ratio was used to apply
viewing windows to the patient’s PET AC image. The liver-based background-
activity ratio was used to apply viewing windows if the tumour or involved nodes
were predominantly surrounded by non-lung tissue. The same viewing window that
was applied to all the phantom transmission scans the patient transmission scans as
uniform viewing window had been established for the phantom transmission images.
110
2.3.3 Results
2.3.3.1 Patient identification and PET scan acquisition protocols
Due to time constraints, the sample size of the 20 patient planning CT and PET
images was not able to be reached. Only 14 patient PET scans were acquired over a
15 month period (August 2004 to November 2005) for whom it was anticipated that
a 3D conformal treatment plan would be required. All of these patients were
positioned on the PET scanner, supine on a flat couch top using the radiotherapy
stabilisation equipment in their intended CT planning and treatment position.
5 of the patient’s images could not be used in the image registration and GTV
definition trials as they did not progress to have a planning CT scan. The results of
their PET scan changed the planned treatment management of these patients and are
summarised in Table 2-11. The remaining 9 patients who did proceed to have a
planning CT scan were positioned for that scan in the exact same position as they
were for their staging PET scan. Table 2-11 provides the clinical history of these 9
patients.
Table 2-11 Patients whose PET images were not used in the clinical trials
Excluded patient Reason for exclusion from the study
1X The patient was being considered for combined chemo and XRT if they had nodal involvement. The PET scan indicated no nodal involvement. The patient had a Lt lower lobe T1N0M0 NSCLC primary that was managed via surgical resection.
2X The PET scan demonstrated multiple metastases. The patient’s stage was upgraded to T4N0M1 with a small Lt upper lobe nodule NSCLC and metastases in the Rt femur and Lt humerus. The patient was managed with chemotherapy only.
3X The patient’s stage was T3N2M0 for a right lung NSCLC following their PET scan and was being considered for combine chemo and XRT. Their planning CT scan demonstrated a collapsed right lung and the patient did not proceed with radiotherapy.
4X The patient’s PET scan demonstrated metastases and the patient was managed with palliative XRT in which no planning CT scan was required for their dosimetry.
5X The patient’s PET scan demonstrated metastases and the patient was managed with palliative XRT in which no planning CT scan was required for their dosimetry.
111
Table 2-12 Summary of the patient clinical data whose images were acquired for the image registration and GTV definition trials
Patient Trial No Bronchoscopy Biopsy Histology Site of disease from PET scan Stage
after PET scan
Treatment following PET PET AC scan
PET transmission
scan
1 Yes Yes NSCLC (undifferentiated)
Recurrence in Rt upper chest wall and upper Rt mediastinum T3N2M0
XRT for local control (Patient had induction chemo and surgical resection 2 years prior to current presentation).
Yes No
2 Yes Yes NSCLC (SCC) Anterior segment of the Lt upper lobe and evidence of Lt hilar nodal involvement T2N1M0 Chemo/XRT (Not a suitable candidate for
surgery). Yes Yes
3 Rt thoracotamy Yes NSCLC 5x7cm subcarinal nodal mass and a Lt pulmonary nodule that was not FDG avid T1N3M0 Palliative Chemo/ XRT Yes No
4 Yes Yes NSCLC (SCC) Anterior Lt upper lobe tumour and Lt pulmonary hilar node involvement. T2N3M0 Chemo/XRT Yes Yes
5 Yes No NSCLC
Extensive malignant mediastinal lymphadenopathy – paratracheal region, anterior and superior mediastinum on the right and in the subcarina to midline. Mild FDG uptake in the right pleural effusion demonstrated on CT.
T2N2M0 Chemo/XRT Yes Yes
6 Yes Yes NSCLC (SCC)
Rt lower lobe – intense uptake in the right postero-inferior lung. No definitive evidence of mediastinal spread however lower grade focal activity in the mediastinum was noted.
T2N2M0 Chemo/XRT Yes No
7 Yes Yes NSCLC (SCC)
Lt upper lobe lesion with involvement in the aorta pulmonary nodes with a suspicious malignant lymph node in the contralateral Rt hilum.
T1N2M0 Palliative Chemo/XRT (Not a suitable candidate for surgery). Yes No
8 No Yes NSCLC Intense uptake in the Rt mid-zone lesion (1cm diameter) with no evidence of hilar or mediastinal nodal involvement.
T1N0M0 XRT (Patient unfit for surgery due to co-morbidities and poor performance status) Yes Yes
9 Yes Yes NSCLC
Intense uptake in a large Lt upper lobe tumour, with extension into the Lt lung apex. The mass was also abutting the Lt hilum and extending into mediastinal structures, but not beyond midline.
T4N2M0 Chemo/XRT Yes Yes
112
2.3.3.2 Image analysis
Lung-based or liver-based background-activity ratios (relative to the GTV or
involved lymph nodes) were determined for each patient’s PET AC image data (see
Table 2-12). The SUV data was not able to be extracted from the PET AC images of
all of the patients. The data in Table 2-12 is based on the maximum pixel values
obtained from the different GTV, lung and liver VOIs.
Table 2-13 Patient GTV, Liver and Lung VOI data
Patient GTV VOI max pixel value
Liver VOI max pixel value
Lung VOI max pixel value
Background-activity ratios Liver-based Lung-based
1 3253 2492 1142 76.6 35.1
2 7944 1731 735 21.8 9.3
3 8022 2212 692 27.5 8.6
4 13288 3123 1369 23.5 10.3
5 16003 3107 1714 19.4 10.7
6 22246 3750 1131 16.9 5.1
7 11263 3101 1202 27.5 10.7
8 13002 3952 1267 30.4 9.7
9 55009 2626 863 4.8 1.6
2.3.3.3 Application of phantom-based image windowing results
Window widths and levels were applied to each patient PET AC image to view the
background and GTV, or the GTV alone, using either the lung or liver-based
background-activity ratios depending on the location of the primary or involved
lymph nodes. Figures 2-33 to 2-35 indicate the window widths and levels applied to
each image and the resulting images. The viewing windows used for patient 1’s PET
AC image were determined based on assumptions made from the phantom PET AC
viewing window results. Patient 1 had two involved lymph nodes in the right upper
chest wall and mediastinum; therefore the liver-based background-activity ratio was
more appropriate to use for applying the viewing windows for this patient. The value
of this ratio (76.6) was almost double the maximum main tank percentage activity
scanned for the phantom (40%). The WW and WL trends for the PET images with
main tank activity 20% and above were followed (i.e as the main tank activity
doubled, 0.1 was added to WW, the WL was kept the same).
113
Figure 2-33 PET image viewing window results: Patients 1 - 3
Patient 1 viewing windows based on non-lung tissue (liver) % of uptake relative to the involved lymph nodes
Patient 1 Fusion WW 0.4 WL 0.0 (%max)
Patient 1 PET AC WW 0.4 WL 0.0 (%max)
Patient 1 Fusion WW 0.4 WL 0.1 (%max)
Patient 1 PET AC WW 0.4 WL 0.1 (%max)
Patient 2 Fusion
WW 0.2 WL 0.0 (%max) Patient 2 PET AC
WW 0.2 WL 0.0 (%max) Patient 2 Fusion
WW 0.2 WL 0.1 (%max) Patient 2 PET AC
WW 0.2 WL 0.1 (%max) Patient 2 PET trans
WW 0.4 WL 0.2 (% max)
Patient 3 viewing windows based on non-lung tissue (liver) % of uptake relative to the involved the primary tumour
Patient 3 Fusion WW 0.2 WL 0.0 (%max)
Patient 3 PET AC WW 0.2 WL 0.0 (%max)
Patient 3 Fusion WW 0.2 WL 0.1 (%max)
Patient 3 PET AC WW 0.2 WL 0.1 (%max)
114
Figure 2-34 PET image viewing window results: Patients 4 – 5
Patient 4 Fusion
WW 0.2 WL 0.0 (%max) Patient 4 PET AC
WW 0.2 WL 0.0 (%max) Patient 4 Fusion
WW 0.2 WL 0.1 (%max) Patient 4 PET AC
WW 0.2 WL 0.1 (%max) Patient 4 PET trans
WW 0.4 WL 0.2 (% max)
Patient 5 Fusion
WW 0.2 WL 0.0 (%max) Patient 5 PET AC
WW 0.2 WL 0.0 (%max) Patient 5 Fusion
WW 0.2 WL 0.1 (%max) Patient 5 PET AC
WW 0.2 WL 0.1 (%max) Patient 5 PET trans
WW 0.4 WL 0.2 (% max)
Patient 6 Fusion WW 0.2 WL 0.0 (%max)
Patient 6 PET AC WW 0.2 WL 0.0 (%max)
Patient 6 Fusion WW 0.2 WL 0.1 (%max)
Patient 6 PET AC WW 0.2 WL 0.1 (%max)
115
Figure 2-35 PET image viewing window results: Patients 7 – 9
Patient 7 Fusion WW 0.2 WL 0.0 (%max)
Patient 7 PET AC WW 0.2 WL 0.0 (%max)
Patient 7 Fusion WW 0.2 WL 0.1 (%max)
Patient 7 PET AC WW 0.2 WL 0.1 (%max)
Patient 8 Fusion
WW 0.2 WL 0.0 (%max) Patient 8 PET AC
WW 0.2 WL 0.0 (%max) Patient 8 Fusion
WW 0.2 WL 0.1 (%max) Patient 8 PET AC
WW 0.2 WL 0.1 (%max) Patient 8 PET trans
WW 0.4 WL 0.2 (% max)
Patient 9 Fusion
WW 0.2 WL 0.0 (%max) Patient 9 PET AC
WW 0.2 WL 0.0 (%max) Patient 9 Fusion
WW 0.2 WL 0.1 (%max) Patient 9 PET AC
WW 0.2 WL 0.1 (%max) Patient 9 PET trans
WW 0.4 WL 0.2 (% max)
116
2.3.4 Discussion and conclusions
Only 9 out of the 14 patients, whose whole-body staging PET scans were obtained
with the patients in their potential radiotherapy treatment position, continued with
their intended 3DCRT treatment planning. Of the 5 patients who were excluded
from the image registration and GTV definition trials, one was down-staged and
underwent surgical resection of the primary tumour instead of 3DCRT. Distant
metastases demonstrated on the PET scans were the most common reason for
changes in the treatment management of the remaining 4 patients. Mac Manus et al27
also reported 30% of patients with NSCLC being considered for radiotherapy were
upstaged due to distant metastases findings on their 18
F-FDG PET staging scans.
It was decided to commence the image registration and GTV definition trials using
the 9 patient PET and CT image data sets due to time constraints and the referral
rates of the lung cancer patients who presented during this time. It had been
estimated that the images of 20 patients with 6 RTs and ROs participating in the
trials would provide statistically significant results. The number of RTs participating
in the image registration trials was doubled to 12 to counteract the effect of halving
of the number of patient image data sets. The number of participating ROs in the
GTV definition trials remained at 6, limiting the contour protocol test to a proof of
concept study.
A protocol was successfully implemented which identified patients in the early
stages of their clinical management as potential candidates for 3DCRT who would
benefit from CT-PET fusion. Alerting the nuclear medicine department of
radiotherapy positioning and image data requirements prior to their whole-body PET
scan via the request slip proved effective. Pan et al’s 116
discussion of some of the
issues of incorporating PET/CT into radiotherapy highlights the importance of
collaboration between nuclear medicine and radiation oncology departments to
achieve accurate patient positioning. Development of a co-ordinated
multidisciplinary approach across multiple departments and institutions was crucial
to ensure that a single PET scan met both the nuclear medicine and radiotherapy
scanning requirements.
117
Practically, collaboration between the nuclear medicine and radiotherapy
departments (which were located in different hospitals) required inter-disciplinary
education. Radiation therapists needed to gain an understanding of whole-body PET
scanning procedures and nuclear medicine technologists needed to understand the
importance of patient positioning in radiotherapy. Initially a radiation therapist
would attend the patient’s PET scan to position the patient in the correct position
using the radiotherapy stabilisation equipment. After the first couple of patients were
scanned the nuclear medicine technologists were sufficiently familiar with the
radiotherapy positioning requirements to confidently position the patients
themselves.
It was important that the correct terminology was used on the PET request form so
that the images supplied on the DVD contained both the attenuation-corrected and
transmission PET images in the 3D DICOM format (particularly as the PET scanner
was off-site). Some of the patient image data sets acquired for this study did not
include transmission scans or SUV data, which was not able to be retrospectively
acquired. However as experience increased in acquiring whole-body PET scans
which were suitable for use in radiotherapy, this was no longer an issue.
As the SUV image data was not available for all the PET AC images, the lung and
liver-based background activities were determined using the pixel values. Based on
the conclusions of the phantom viewing window tests, the ratio of the activity in the
normal tissue immediately surrounding an object were used to select viewing
windows. Maximum pixel values within the tumour and its surrounding normal
tissues were used to determine background-activity ratios (as per the phantom image
quantification results and the literature56
supporting this value as a true indication of
uptake).
The uptake in the normal mediastinal tissues proximal to the primary tumour or
involved lymph nodes in this region of the chest would seem a more appropriate
choice to determine a background-activity ratio rather than a liver-based ratio.
Paquet et al’s117 study of within-patient variability of 18F-FDG uptake demonstrated
that uptake in normal mediastinal tissue is equivalent to that in normal liver tissue.
Due to difficulties in sampling the uptake in the normal mediastinal tissues without
118
including uptake from either the left ventricle or the lung cancer itself, the maximum
pixel values within a volume of interest within the liver was used as a substitute for
mediastinal uptake. It is important that the proximal lung tissue is used to determine
background-activity ratios for primary tumours or involved lymph nodes surrounded
by lung tissue. Wang et al118
found that uptake is non-uniform between the right and
left lungs and the different lobes of the same lung.
The viewing window protocols from the phantom image tests were easily applied to
the patient PET images. A WW=40% and WL=20% was applied to each PET
transmission image as per the phantom image test results. Lung-based background-
activity ratios were used to select the viewing windows for 6 of the 9 patient PET AC
images. Liver-based background-activity ratios were applied to the AC images for
patients 1, 3 and 9 where the primaries or involved lymph nodes were mostly
surrounded the mediastinum or chest wall. The general PET viewing window to
display both the background and tumour uptake of WW = 20% and WL = 0% was
applied to all but patient 1’s PET AC images (i.e. the background-activity ratios were
less than 40%).
A WW = 20% and a WL = 10% to display the tumours without the proximal
background uptake was applied the AC images for patients 2 – 9. While the
phantom image tests found a WL = 5 – 10% for the different background ratios, no
visible difference could be found in applying either a 5% or 10% WL to the patient
images. As Hong et al’s114
study based on patient images found that narrowing the
WL to within 10% of the WW allowed tumours to be displayed with reasonably
sharp edges which correlated with their dimensions on CT, this approach was taken
to simplify the application of the phantom viewing window results to patient images.
The phantom images did not simulate every possible patient specific condition hence
some assumptions were made when applying viewing windows to the patient images.
These assumptions involved extrapolation of the results for the imaged phantom
conditions, which was demonstrated by the choice of viewing windows chosen for
displaying patient 1’s AC images. The phantom viewing window tests demonstrated
that the window levels for viewing the moving sphere required a WW=10% and
WL=0%. While the entire chest is under influence of respiration, it needs to be noted
119
that the moving sphere was surrounded by air on the phantom and that none of the
tumours in the patient images were surrounded by tissues with 0% background
activities. Taking these factors into account it was assumed that motion effects
would be counterbalanced by the fact that that the viewing window tests also
demonstrated that as background increased, WW increased.
120
3
3.1 Phantom based image registration technique evaluation
Image registration technique evaluation and
development of a protocol for the clinical trials
3.1.1 Aims
Various processes can be used when registering CT and PET images. Using the
previously acquired PET and CT scans of the phantom, the following will be
evaluated:
1. The relative level of accuracy of the CC and MI image registration algorithms.
2. The level of accuracy and reproducibility of the following registration
techniques:
• Using the MI algorithm to auto-register the PET AC scan with the
planning CT scan, compared to,
• Using the MI algorithm to auto-register the PET transmission scan with
the planning CT scan and using the post-registration parameters of the
transmission scan to register then PET AC scan (transmission scan-based
registration).
3. The effects of the differences in the imaged dimensions of the moving sphere
between the PET and CT images on the level of accuracy and reproducibility of
automated registration techniques.
4. The efficacy of fiducial markers in the image registration process for confirming
the accuracy of registration outcomes by localising the fiducial markers placed on
the phantom for each of the CT and PET scans of the phantom on pre-registered
image data sets of the phantom.
121
3.1.2 Methodology
3.1.2.1 Algorithm tests
The phantom PET AC scan 1a (scan (a) from series 1 – see Table 2-4 Chapter 2) was
imported twice into the same plan in the Pinnacle
Reproducibility tests of the automated registration software
3
TPS creating a perfectly registered
pair of images. Using the Syntegra Image Fusion platform of the TPS, one of the
images (the secondary data set) was offset from the other (the primary image data
set) using translation and rotation offsets about each of the image axes.
Table 3-1 Pre-registration offsets of secondary image from the primary for algorithm reproducibility tests
Condition
Secondary image pre-registration start point offsets from the origin
Translation (cm) offset Rotation (degrees) offset
x y z x y z
1 2 2 2 1 1 1
2 -2 -2 -2 -1 -1 -1
The CC algorithm was used to automatically register the two data sets of the same
image for two different pre-registration offset conditions (see Table 3-1). Once the
Syntegra software had registered the two data sets, the x, y and z axes translation and
rotation parameters of the secondary image relative to the primary were recorded
post-registration. This process was then repeated so that a total of 5 registrations for
each pre-registration offset were performed.
The entire process of offsetting the data sets prior to registration for conditions 1 and
2 and registering the data sets from these offsets 5 times was then performed using
the MI algorithm.
122
The CC and MI algorithms were used to register the following data sets:
Cross correlation and mutual information algorithm tests
1. CT1 and CT1
• Two copies of the same CT scan of the phantom (scan no.1 from the static
series of CT scans – see Table 2-2 Chapter 2)
2. PET AC 1a and PET AC 1a
• Two copies of the same PET AC scan of the phantom (Scan (a) from PET
series 1 – see Table 2-4 Chapter 2)
3. PET AC 1a and PET AC 1b
• Two PET AC scans of the phantom from the same series (Scan (a) and
scan (b) from PET series 1 – see Table 2-4 Chapter 2)
Initially each of the secondary data sets for the pairs of data sets above was offset
with translation parameters for the x, y and z axes only (conditions 1-10 of Table 3-
2) prior to registration. The data set pairs were then registered using the Pinnacle3
Syntegra software, using the CC and then the MI algorithms.
Table 3-2 Translation only pre-registration offsets of secondary image from the primary for algorithm accuracy tests
Condition
Secondary image pre-registration start point offsets from the origin
Translation (cm) offset Rotation (degrees) offset
x y z x y z
1 1 1 1 0 0 0 2 -1 -1 -1 0 0 0 3 2 2 2 0 0 0 4 -2 -2 -2 0 0 0 5 3 3 3 0 0 0 6 -3 -3 -3 0 0 0 7 4 4 4 0 0 0 8 -4 -4 -4 0 0 0 9 5 5 5 0 0 0 10 -5 -5 -5 0 0 0
Rotational offsets for the x, y and z axes were then added to pre-registration
translation offsets. For each of the translation offsets used previously, rotation
123
offsets were applied, starting at 0.5° and increasing to 2.0° in 0.5° increments. Extra
rotation offsets were applied for the translation offsets of 5 and -5 (see Table 3-3).
Table 3-3 Translation and rotation pre-registration offsets of secondary image from the primary for algorithm accuracy tests
Condition
Secondary image pre-registration start point offsets from the origin
Translation (cm) offset Rotation (degrees) offset
x y z x y z
1a-d 1 1 1 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 2a-d -1 -1 -1 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 3a-d 2 2 2 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 4a-d -2 -2 -2 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 5a-d 3 3 3 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 6a-d -3 -3 -3 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 7a-d 4 4 4 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 8a-d -4 -4 -4 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d) 0.5(a), 1(b), 1.5(c), 2(d)
9a-g 5 5 5 0.5(a), 1(b), 1.5(c), 2(d), 3(e), 4(f) 5(g)
0.5(a), 1(b), 1.5(c), 2(d), 3(e), 4(f) 5(g)
0.5(a), 1(b), 1.5(c), 2(d), 3(e), 4(f) 5(g)
10a-g -5 -5 -5 0.5(a), 1(b), 1.5(c), 2(d), 3(e), 4(f) 5(g)
0.5(a), 1(b), 1.5(c), 2(d), 3(e), 4(f) 5(g)
0.5(a), 1(b), 1.5(c), 2(d), 3(e), 4(f) 5(g)
3.1.2.2 Baseline registrations
Pairs of CT and PET images of the phantom listed in Table 3-4 were imported into
Pinnacle3
for registration, with the CT data set always designated as the primary
image and the PET scan as the secondary image. The CT and PET data sets were
paired according to phantom conditions (i.e. main tank full or empty, the presence of
the sphere, or whether the sphere was static or moving). Note that when a free
breathing series of CT scans was registered with a PET series, each scan in the free
breathing CT scan series was registered with each AC scan in the PET series.
Baseline manual registrations were performed for each of the pairs of data sets,
ensuring that the main tank of the phantom was correctly registered in each case.
The post-registration translations and rotations about the x, y and z axes were
recorded for each manual registration.
124
Table 3-4 Registered CT and PET AC scans of the phantom
CT scans of phantom (Primary data set)
PET AC scan no of phantom from the different series of PET scans
(Secondary data sets) registered with each primary CT scan
Static scan no 1
The main tank alone with the central rods filled with water and the main tank empty 1a 1b 1c 1d 1e 1f
Static scan no 1
The main tank alone with the central rods filled with water and the main tank empty 2a 2b 2c 2d 2e 2f
Static scan no 3
The main tank with the central rods filled with water and the main tank empty and the sphere filled with water in the static position
3a 3b 3c 3d 3e 3f
Free breathing Series 3
scans
5 scans with the main tank with the central rods filled with water and the main tank empty and the sphere filled with water oscillating at 20cycles/min
Scan 1 4a 4b 4c 4d 4e 4f
Scan 2 4a 4b 4c 4d 4e 4f
Scan 3 4a 4b 4c 4d 4e 4f
Scan 4 4a 4b 4c 4d 4e 4f
Scan 5 4a 4b 4c 4d 4e 4f
Static scan no 2
The main tank alone with both the central rods and the main tank filled with water 5a 5b 5c 5d 5e 5f
Free breathing Series 4
scans
5 scans with the main tank with both the central rods and the main tank filled with water and the sphere filled with water oscillating at 20cycles/min
Scan 1 6a 6b 6c 6d 6e 6f
Scan 2 6a 6b 6c 6d 6e 6f
Scan 3 6a 6b 6c 6d 6e 6f
Scan 4 6a 6b 6c 6d 6e 6f
Scan 5 6a 6b 6c 6d 6e 6f
Free breathing Series 4
scans
5 scans with the main tank with both the central rods and the main tank filled with water and the sphere filled with water oscillating at 20cycles/min
Scan 1 7a 7b 7c 7d 7e 7f
Scan 2 7a 7b 7c 7d 7e 7f
Scan 3 7a 7b 7c 7d 7e 7f
Scan 4 7a 7b 7c 7d 7e 7f
Scan 5 7a 7b 7c 7d 7e 7f
Free breathing Series 4
scans
5 scans with the main tank with both the central rods and the main tank filled with water and the sphere filled with water oscillating at 20cycles/min
Scan 1 8a 8b 8c 8d 8e
Scan 2 8a 8b 8c 8d 8e
Scan 3 8a 8b 8c 8d 8e
Scan 4 8a 8b 8c 8d 8e
Scan 5 8a 8b 8c 8d 8e
125
3.1.2.3 Auto-registration of the planning CT and PET AC scans using the MI
algorithm
The same pairs of CT and PET image data sets (as per Table 3-4) were imported into
separate plans. Prior to automatically registering each pair of images, the PET AC
scan was centred on the CT scan (i.e. the midpoint of the 3D image coordinates of
each were aligned). This ensured that there was sufficient overlap of the two data sets
prior to registration. Each of the pairs of data sets were then registered using the
Syntegra automated registration software, using the MI algorithm. The post-
registration x, y and z axes translation and rotations were recorded.
3.1.2.4 Automated PET transmission scan-based registration with the planning
CT scan using the MI algorithm
The same pairs of CT and PET image data sets (as per Table 3-4) were imported into
separate plans, as well as the associated PET transmission scans for each of the AC
scans. This resulted in the primary CT scan of the phantom as well as two secondary
PET images in each plan. The procedure for registering the transmission scan then
the AC scan was as follows:
1. The transmission scan was nominated as the secondary data set
2. The transmission scan was then centred on the CT scan
3. The transmission scan was then registered with the CT scan using the Syntegra
software and the MI algorithm.
4. The post- registration x, y and z axes translations and rotations were copied to the
AC scan, resulting in registration of AC scan with the CT based on the
registration results using the transmission scan.
The post-registration x, y and z axes translation and rotations for these registrations
were recorded.
3.1.2.5 Fiducial marker tests
To test the accuracy of the fiducial markers for point based registration and
confirming registration, the following series of baseline registrations were used:
126
• Static CT scan no 1 and PET Series 1 AC scans
• Static CT scan no 3 and PET Series 3 AC scans
• Free breathing CT scan series 3- scan 1 and PET Series 4 AC scans
• Static CT scan no 2 and PET Series 5 AC scans
• Free breathing CT scan series 4- scan 1 and PET Series 7 AC scans
• Free breathing CT scan series 4- scan 1 and PET Series 8 AC scans
For each of the manual baseline registrations above, the Syntegra point-based
registration tools were used to localise the centre of the two fiducial markers placed
on the phantom during the acquisition of each CT and PET scans. The following
steps were used to localise the centre of the imaged fiducial marker on each of the
registered CT and PET AC scans:
1. Two sets of fiducial marker “point pairs” were created for each registered CT
and PET AC scan (i.e. one point pair for the right marker and one point pair for
the left marker)
2. The centre of the left and right fiducial markers (placed on top of the phantom)
was localised on the CT scan of the phantom by placing the first point of each of
the point pairs in the centre of each of the fiducial markers. The centre of the
marker is the 5 mm axial hole in the middle of the IZI MM3003 multi-modality
fiducial markers on the slice with the z coordinate = 0 (the CT couch coordinates
were zeroed in the sup-inf (z coordinate) direction when the phantom was CT
scanned.
3. The centres of both the left and right fiducial markers were then localised on the
registered PET AC scan of the phantom. The centre of each of the markers was
defined by scrolling through the slices of the PET AC scans in the sup-inf
direction and determining the slice half-way between the most superior and
inferior slices that the fiducial marker was imaged on. The point was then placed
in the middle of the intense region where the 18-FDG was injected into the 5 mm
axial hole of each marker on this slice.
127
3.1.3 Data analysis
3.1.3.1 Cross correlation and mutual information algorithm tests
The x, y and z axes translation and rotation parameters of the secondary image
relative to the primary were recorded post-registration. These results were converted
to absolute values. Scatter plots of the absolute values were generated so that the
effect of increasing the pre-registration translation and rotation offsets on the
magnitude of the post-registration translation and rotation parameters using different
registration algorithms could be evaluated.
3.1.3.2 Comparison of auto-registration results of the phantom CT and PET
images
The post-registration translation and rotation values for each of the image axes for
each set of CT and PET image registration were subtracted from the baseline
registrations of the same pairs of data sets. This provided the difference in the post-
registration x, y and z translation and rotation parameters between the baseline
registrations and each of the automatic registrations.
Calculation of the post-registration results relative to the baseline registrations
Box plots of the post-registration translation and rotation parameters relative to the
baseline registrations were generated to demonstrate the spread of the automated
registration results relative to the baseline registrations. The registration results were
grouped by the PET scan series of the phantom as each series was acquired with
varying “patient specific” conditions. Where each of the 5 scans in a free breathing
CT scan series was registered with a PET series in which the sphere was moving
during acquisition (see Table 3-4), box plots were created with the registrations
results grouped by the CT scan. Series of box plots were grouped by the following
registration results:
Box plots of the distribution of the post-registration results relative to the
baseline registrations
• Intra-PET series results for the registrations where the CT and PET AC
scan only were used for automatic registration.
128
• Intra-PET series results for the registrations where the PET transmission
scan registration parameters with the CT scan formed the basis of the PET
AC scan registration.
• Combined registration results from the series where the components of
the phantom were static for:
The PET AC scan-based automatic registrations
The PET transmission scan-based automatic registrations
• Combined registration results from the series where the sphere was
moving for:
The PET AC scan-based automatic registrations
The PET transmission scan-based automatic registrations
The mean and standard deviation of the post-registration differences from the
baseline registrations x, y and z axes were calculated, with the data grouped by
registrations for the different PET scan series of the phantom. This data was used to
create scatter plots of standard deviation against the mean as described by Bland and
Altman
Plots of the standard deviation against the mean for the registration results
119
• Assessment that there is not a systematic error in the repeated registration
of CT and PET images for the same phantom conditions (i.e. as the mean
difference from the baseline registrations increases the standard deviation
should not increase).
and using the “true value is constant” method. This allows for:
• A graphical means of assessing the level of reproducibility of the different
registration methods.
The closer the standard deviation for a group of registrations is to
zero the more reproducible the method.
As this method assumes that the true value (in this case the post-registration results)
should be constant, there is no pairing of the data between the different registration
methods. The following plots of the standard deviation against the mean for the
differences from the baseline registrations were created for:
129
• The post-registration x, y and z translations and then the post-registration
x, y and z rotations for:
The combined PET AC scan-based registrations
The combined PET transmission scan-based registrations
• The post-registration x, y and z translations and then the post-registration
x, y and z rotations where the components of the phantom were static for:
The PET AC scan-based registrations
The PET transmission scan-based registrations
• The post-registration x, y and z translations and then the post-registration
x, y and z rotations where the sphere was moving for:
The PET AC scan-based registrations
The PET transmission scan-based registrations
The repeatability coefficient as described by Bland and Altman
Calculation of the repeatability coefficient 120
was used to
quantify the level of reproducibility of the registration results. The repeatability
coefficient (rpt coeff) is defined as:
ws 21.96 coeffrpt =
= 2.77 s
where sw
w
= the within subject standard deviation
The within subject standard deviation is obtained by:
1. Performing one way analysis of variance with the subject as the factor
2. The mean of the within subject variances for a method is then determined
to obtain sw
2
The repeatability coefficient provides the level of magnitude that any two
registrations of the phantom CT and PET images will be within. The magnitude of
the coefficient is relative to the units of the registration parameters (i.e. centimetres
and degrees for the post-registration translation and rotation parameters
respectively). Repeatability coefficients were calculated with the registrations results
130
grouped by the different image based registration techniques and the presence or
absence of the moving sphere in the registered images.
The mean and standard deviation was calculated using the absolute values for each of
the post-registration translation and rotation parameters. The registration results
from all the intra-PET series registrations were combined. A paired t-test was used
to compare the means for each of the post-registration parameters between the AC
scan and transmission scan-based registration methods. To determine whether
motion influences registration outcomes, a t-test was performed comparing the intra-
PET series registration results where all the phantom components were static to the
intra-PET series registration results where the sphere was moving. The t-tests were
performed separately for the AC scan-based and transmission scan-based registration
results.
Comparison of the mean registration results
3.1.3.3 Fiducial marker tests
The centres of the fiducial markers on the CT scan of the phantom were able to be
accurately located due to the method of acquisition of the CT images of the phantom.
As each CT image was acquired, the couch longitudinal position was “zeroed” at the
centre of the fiducial markers. This resulted in a slice being imaged through the
centre of the markers and as well as an image coordinate of z = 0 corresponding to
the centre of the fiducial markers on the CT images. With the PET AC images
registered with each CT scan the point based Syntegra software tools were used to
analyse the accuracy of the fiducial marker localisation on the PET AC images of the
phantom. The following information was extracted from the TPS:
3. The distance between the centres of each of the individual fiducial marker
point pairs.
4. The mean distance between the all of the point pairs placed on the CT and
the PET image for both the left and right markers.
The within-series mean and standard deviation of the “distance between pairs” value
and the “mean distance” value were calculated.
131
3.1.4 Results
3.1.4.1 Algorithm tests
Repeated registrations with the same pre-registration translation and rotation offsets
applied to the axes of a registered secondary image provided the same post-
registration results for all 5 registrations. This was found to be the case when using
either the CC or MI algorithm to perform the automated registrations.
Reproducibility tests of the automated registration software
The various scatter plots of the absolute values demonstrated the effect of increasing
the pre-registration translation and rotation offsets on the magnitude of the post-
registration translation and rotation parameters using either the CC or MI registration
algorithms. Appendix 2 contains the graphs for the registration results for the
different sets of images and the algorithm used to register the images. Note that
there are no graphs for the post-registration results for registering the same CT image
with itself using the CC algorithm as each pre-registration offset resulted in perfect
registration (i.e. all post-registration parameters = 0 (cm or degrees). Table 3-5
provides a summary of the algorithm tests based on the scatter plot observations and
the raw data tests as a function of algorithm, applied pre-registration offsets and the
images registered.
Cross correlation and mutual information algorithm tests
While some deviations (or outliers) could be seen throughout the results of the
algorithm tests, there were some general patterns observed across the post-
registration results. For the two algorithms these were:
• The CC algorithm resulted in:
Perfect registration where the same image was registered with
itself.
Identical registration results when the two different PET images of
the same phantom condition were registered, with very small
magnitudes of post-registration parameter values.
The same registration results except where the pre-registration
translation offsets reach a magnitude of 5 cm.
132
Table 3-5 Summary of the results of the CC and MI algorithm tests Algorithm Pre-registration offsets Registered images Summary of post-registration results
CC algorithm
Applied to the translation parameters only
CT1 and CT1 Each pre-registration offset resulted in perfect registration (i.e. all post-registration parameters = 0 cm or degrees).
PET AC 1a and 1a The post-registration values = 0 (cm or degrees) except for pre-registration offsets of 5 cm and -5 cm. For these offsets, the magnitude of the post-registration values ranged from 2.31 – 10.55 cm and 0.79 – 12.90° for the translation and rotation parameters respectively.
PET AC 1a and 1b The post-registration offsets for each parameter were the same for each registration (x trans= -0.01 cm; y trans= 0.02 cm; z trans = -0.03 cm; x rot= 0.12°; y rot= -0.01°; z rot= -0.04°) except the pre-registration offsets for 5 cm and -5 cm. For these offsets, the magnitude of the post-registration values ranged from 2.73 – 9.15 cm and 2.04 – 11.44° for the translation and rotation parameters respectively.
Increasing translation and rotation pre-registration offsets applied
CT1 and CT1 The same post-registration results were observed as for those where the registration of these images was performed with translation only pre-registration offsets.
PET AC 1a and 1a The same post-registration results were observed as for those where the registration of these images was performed with translation only pre-registration offsets. For the 5 cm and -5 cm translations with increasing rotation offsets, the magnitude of the post-registration values ranged from 1.97 – 12.57 cm and 0.16 – 14.47° for the translation and rotation parameters respectively.
PET AC 1a and 1b The same post-registration results were observed as for those where the registration of these images was performed with translation only pre-registration offsets. For the 5 cm and -5 cm translations with increasing rotation offsets, the magnitude of the post-registration values ranged from 1.12 – 11.55 cm and 0.73 – 18.37° for the translation and rotation parameters respectively.
MI algorithm
Applied to the translation parameters only
CT1 and CT1 Post-registration offsets were observed for the translation parameters ranging from 0 – 0.13 cm. Each of the rotation post registration values = 0 (cm or degrees).
PET AC 1a and 1a Post registration offsets were observed ranging from 0 – 0.07 cm and 0 - 0.36° for each of the translation and rotation axes respectively.
PET AC 1a and 1b Post registration offsets were observed ranging from 0.01 – 0.27 cm and 0 - 0.58° for the translation and rotation axes respectively except the -4 cm pre-registration offsets. For this offset the magnitude of the post-registration values ranged from 0.22 – 3.39 cm and 0.34 – 22.81° for the translation and rotation axes respectively.
Increasing translation and rotation pre-registration offsets applied
CT1 and CT1 The post- registration values were observed ranging from 0 – 0.24 cm and 0 - 0.5° for the translation and rotation axes respectively, except the -4 cm, 5 cm and -5 cm translations with increasing rotation pre-registration offsets. For these offsets, the magnitude of the post-registration values ranged from 0 – 8.57 cm and 0 –89.99° for the translation and rotation axes respectively.
PET AC 1a and 1a The post- registration values were observed ranging from 0 – 0.12 cm and 0 - 0.71° for each of the translation and rotation axes respectively, except the -5 cm translation with increasing rotation pre-registration offsets. For these offsets the magnitude of the post-registration values ranged from 0 – 3.01 cm and 0.01 – 18.93° for the translation and rotation axes respectively.
PET AC 1a and 1b
The post- registration values were observed ranging from 0 – 0.26 cm and 0.02 – 1.02° for each of the translation and rotation axes respectively for the pre-registration offsets with translations less than a magnitude of 3cm. For the translation offsets equal to or greater than a magnitude of 3 cm, the post-registration values ranged from 0 – 6.63 cm and 0.01 – 28.4° for the translation and rotation axes respectively.
133
• The MI algorithm resulted in:
A magnitude of post-registration parameters less than 0.3 cm for
the translation values and, in all but one case, less than 1.0° for the
rotation parameters.
A significantly higher magnitude of post-registration parameters
for the two different PET images of the same phantom condition
were registered with pre-registration translation offsets equal to or
greater than 3 cm with rotation offsets applied.
Both algorithms demonstrated that of the 6 registration parameters, the z translation
axis and the x rotation axis had the largest magnitude of post-registration offset
values, except where the pre-registration translation offsets of higher magnitude
produced large registration errors.
3.1.4.2 Comparison of the level of accuracy and reproducibility of AC scan-
based registration versus transmission scan-based-registration
Appendix 3 contains the box plots created for the intra-PET series registrations for
both of the automated registration techniques. The registration results are relative to
the baseline registrations (where it is known that the main tank of the phantom was
correctly aligned for both the CT and the PET scans). The box plots display the
registration results as follows (see Figure 3-1):
• The 25th and 75th percentiles of the post-registration parameters as the
lower and upper lines of the box.
• The median of each of the post-registration parameters as the red line in
the middle of the box.
• The extent of the rest of the post-registration parameters is shown as
black lines extending above and below the box.
• Any post-registration parameter that is more than 1.5 times the inter-
quartile range is shown as a red plus sign.
The intra-series box plots demonstrated that the transmission scan-based registration
results were more narrowly distributed relative to the baseline registration than the
results for the AC scan-based registrations. This is demonstrated by consistently
134
lower magnitude of the inter-quartile ranges of the post-registration parameters for
the transmission-scan-based registrations. The median for each post-registration
parameter does not display any obvious level of difference between the two
registration methods, when all the intra-series registration plots are examined
together.
Figure 3-1 Box plots for the registration results for the series 2 PET images of the phantom The plot for AC scan-based registration results for the different translation and rotation registration parameters is shown in (a), while the plot for the transmission-scan-based registration results is shown in (b). (Note: n=24 registrations for each parameter).
(a)
(b)
135
Figure 3-2 contains the plots of the standard deviation versus the mean of the post-
registration parameters relative to the baseline registrations for the registered images
based on the PET image used in the automated registration process. These graphs
demonstrate:
• There isn’t an apparent systematic error in the repeated registration of CT
and PET images using either the transmission or AC scan-based
registration technique (i.e. as the mean difference from the baseline
registrations increases the standard deviation does not increase).
The graph of the post-registration rotation parameters for the AC
scan-based registrations does have three points with a high mean
and standard deviation that could suggest otherwise.
• The magnitudes of the standard deviation for the different post-
registration parameters indicate that the transmission scan-based
registrations are more reproducible than the AC scan-based registrations.
Figure 3-2 The standard deviation plotted against the mean for the post registration parameters: combined AC and transmission scan-based registrations There are n=24 registrations for each parameter of the images from PET series 1-3 and 5. There are n=120 registrations for each parameter of the images from PET series 4, 6-8.
PET AC registrations
0
0.5
1
1.5
2
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5mean difference (cm)
stan
dar
d d
evia
tion
(cm
) x transy transz trans
PET AC registrations
0
1
2
3
4
5
6
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
mean difference (degrees)
stan
dar
d d
evia
tion
(d
egre
es)
x roty rotz rot
PET trans registrations
0
0.5
1
1.5
2
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5mean difference (cm)
stan
dar
d d
evia
tion
(cm
) x transy transz trans
PET trans registrations
0
1
2
3
4
5
6
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5mean difference (degrees)
stan
dar
d d
evia
tion
(d
egre
es)
x roty rotz rot
136
The observations made from the plots of the standard deviation versus the mean are
confirmed by the repeatability coefficients in Table 3-6. The repeatability
coefficients provide the levels of magnitude for each registration parameter that any
two registrations will be within based on the registration results for each registration
technique. The repeatability coefficients are consistently smaller for the transmission
scan based-registration parameters compared to those for the AC scan-based
registrations.
Table 3-6 Repeatability coefficients for the different image-based automated registration techniques
Registration technique Repeatability coefficient
x trans (cm)
y trans (cm)
z trans (cm)
x rot (degrees)
y rot (degrees)
z rot (degrees)
PET AC scan-based registration 0.742 0.528 1.788 5.599 5.120 3.975
PET trans scan-based registration 0.419 0.237 0.498 0.676 1.242 0.514
Direct comparison of the means of the registration results for the two different
image-based registration techniques indicates that the transmission scan-based
technique has the higher level of accuracy, relative to the baseline registrations. The
means of the absolute values for the different post-registration parameters are smaller
for the transmission scan-based registrations compared those for the AC scan-based
registrations (see Table 3-7). The results of the paired t-tests of the means for the
different post-registration parameters (also shown in Table 3-7) demonstrate that
means are significantly different for 4 out of the 6 parameters (p < 0.05).
137
Table 3-7 Paired t-test comparisons of AC and transmission scan-based mean registration results of the phantom CT and PET images
x trans (cm)
y trans (cm)
z trans (cm)
x rot (degrees)
y rot (degrees)
z rot (degrees)
PET AC scan-based registrations
mean 0.23 0.28 0.34 0.63 0.69 0.68
standard deviation 0.18 0.16 0.40 1.24 1.18 0.80
PET trans scan-based registrations
mean 0.22 0.13 0.21 0.40 0.59 0.37
standard deviation 0.21 0.12 0.15 0.43 0.44 0.47
Comparison of means
significant difference no yes yes yes no yes
p 0.56 <0.001 0.003 0.03 0.3 <0.001
3.1.4.3 The effects of motion on the level of accuracy and reproducibility of
automated registration techniques
Figure 3-3 contains the box plots of the combined registration results for the CT and
PET images where the components of the phantom were static. Figure 3-4 contains
the box plots of the combined registration results of the images where the sphere was
moving during acquisition. When the post-registration parameters relative to the
baseline registrations are grouped according to the absence or presence of motion in
the phantom:
• The AC scan-based registrations resulted in more widely distributed post-
registration parameters for registration of the images where the phantom
components were static compared to the registered images where the
sphere was moving.
• A decrease in the distribution of the post-registration parameters is seen
for the transmission scan-based registrations compared to the AC scan-
based registrations for both the static and moving sphere conditions of the
phantom.
While the x translation post-registration parameter for the
registrations of images with the moving sphere has a narrower
distribution of results, there is an increase in the number of
outliers (see Figure 3-4).
138
Figure 3-3 Combined registration results for the CT and PET images with all components of the phantom static There are two plots for the AC-based-registrations, one with the full range of the outliers shown, the other with a y-axis range the same as for the transmission-scan-based registrations (Note: n=24 registrations for each parameter).
139
Figure 3-4 Combined registration results for the CT and PET images with the sphere moving Note: n= 120 registrations for each parameter
140
Figures 3-5 and 3-6 contain the plots of the standard deviation versus the mean of the
post-registration parameters relative to the baseline registrations for the registered
images where the components of the phantom were static or where the sphere was
moving during acquisition. These graphs demonstrate:
• There isn’t an apparent systematic error in the repeated registration of CT
and PET images for the different phantom conditions using either the
transmission or AC scan-based registration technique
The graph of the post-registration rotation parameters for the AC
scan-based registrations with static images does have three points
with a high mean and standard deviation that could suggest
otherwise (see Figure 3-5).
• The magnitudes of the standard deviation of the different post-registration
parameters indicate that the transmission scan-based registrations are
more reproducible than the AC scan-based registrations for both static
and the moving sphere images.
The graph of the post-registration translation parameters for the
transmission scan-based registrations with the moving sphere has
one outlier. One of the x translation parameters has a standard
deviation approximately 4 times larger than the other translation
parameters (see Figure 3-6).
141
Figure 3-5 The standard deviation plotted against the mean for the post registration parameters – all components of phantom static The PET images are from series 1-3 and 5. There are n= 6 registrations PET scans with each static CT (represented by each point in the graphs). There are 4 series of PET images where all components were static = 4 points for each parameter).
Static phantom components: PET AC registrations
0
0.5
1
1.5
2
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5mean difference (cm)
stan
dar
d d
evia
tion
(cm
) x transy transz trans
Static phantom components: PET AC registrations
0
1
2
3
4
5
6
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5mean difference (degrees)
stan
dar
d d
evia
tion
(d
egre
es)
x roty rotz rot
Static phantom components: PET trans registrations
0
0.5
1
1.5
2
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5mean difference (cm)
stan
dar
d d
evia
tion
(cm
) x transy transz trans
Static phantom components: PET trans registrations
0
1
2
3
4
5
6
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5mean difference (degrees)
stan
dar
d d
evia
tion
(d
egre
es)
x roty rotz rot
Figure 3-6 The standard deviation plotted against the mean – with the sphere moving during scanning The PET images are from series 4, and 6-8. There are n = 6 registrations of PET scans with each CT scan in the free breathing series (represented by each point in the graphs). There are 5 CT scans in a free breathing CT series and 4 PET series where the sphere was moving=20 points for each parameter.
Moving sphere: PET AC registrations
0
0.2
0.4
0.6
0.8
1
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5mean difference (cm)
stan
dar
d d
evia
tion
(cm
) x transy transz trans
Moving sphere: PET AC registrations
0
0.5
1
1.5
2
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5mean difference (degrees)
stan
dar
d d
evia
tion
(d
egre
es)
x roty rotz rot
Moving sphere: PET trans registrations
0.0
0.2
0.4
0.6
0.8
1.0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5mean difference (cm)
stan
dar
d d
evia
tion
(cm
) x transy transz trans
Moving sphere: PET trans registrations
0.0
0.5
1.0
1.5
2.0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5mean difference (degrees)
stan
dar
d d
evia
tion
(d
egre
es)
x roty rotz rot
142
The repeatability coefficients in Table 3-8 demonstrate that:
• The AC scan-based registrations of the images with the moving sphere
are more reproducible than those with all the phantom components static.
• The transmission scan-based registrations have a similar level of
reproducibility for both the static and moving sphere images.
3 of the post-registration parameters (the x translation and the x
and z rotations) have smaller coefficients for the AC scan-based
registrations.
3 of the post-registration parameters (the y and z translations and
the y rotation) have smaller coefficients for the transmission scan-
based registrations.
• The transmission scan-based registrations are more reproducible than the
AC scan-based registrations for both the static and the moving sphere
images (the x translation parameter being the exception).
Table 3-8 Repeatability coefficients for the static phantom or moving sphere images
Phantom condition and registration technique
Repeatability coefficient
x trans (cm)
y trans (cm)
z trans (cm)
x rot (degrees)
y rot (degrees)
z rot (degrees)
Static components PET AC scan-based registration 0.988 0.580 2.459 7.838 7.162 5.244
Static components PET trans scan-based registration 0.148 0.226 0.636 0.313 1.657 0.492
Moving sphere PET AC scan-based registration 0.334 0.481 0.594 1.106 1.091 1.991
Moving sphere PET trans scan-based registration 0.534 0.125 0.269 0.747 0.553 0.538
Direct comparison of the means of the registration results (relative to the baseline
registrations) for the static phantom and moving sphere images demonstrates that
there is no overall difference between the levels of accuracy due to motion as:
• The means for 4 out of the 6 registration parameters are higher for the AC
scan-based registrations of the static phantom images.
143
• The means for 3 out of the 6 registration parameters are higher for the AC
scan-based registrations of the moving sphere phantom images.
• The means for 3 out of the 6 registration parameters are higher for the
transmission scan-based registrations of the moving sphere phantom
images.
The t-test results demonstrate that:
• For the AC scan-based registrations that there is no significant difference
between the means of the registered static phantom or moving sphere
images for 3 out of the 6 registration parameters.
• For the transmission scan-based registrations that there is no significant
difference between the means of the registered static phantom or moving
sphere images for 4 out of the 6 registration parameters.
Table 3-9 T-test comparisons of static or moving sphere for the AC and transmission scan-based mean registration results of the phantom CT and PET images PET AC scan-based registrations
x trans (cm)
y trans (cm)
z trans (cm)
x rot (degrees)
y rot (degrees)
z rot (degrees)
Static phantom
mean 0.21 0.15 0.80 0.98 0.82 1.32 standard deviation 0.26 0.15 0.74 2.72 2.46 1.29
Moving sphere
mean 0.24 0.31 0.24 0.56 0.66 0.55 standard deviation 0.16 0.15 0.18 0.59 0.68 0.57
Comparison of means
significant difference no yes yes no no yes
p 0.48 <0.001 0.001 0.46 0.8 0.008
PET trans scan-based registrations
x trans (cm)
y trans (cm)
z trans (cm)
x rot (degrees)
y rot (degrees)
z rot (degrees)
Static phantom
mean 0.15 0.17 0.19 0.25 0.45 0.99 standard deviation 0.12 0.13 0.25 0.23 0.58 0.83
Moving sphere
mean 0.24 0.12 0.22 0.43 0.62 0.25 standard deviation 0.22 0.11 0.12 0.46 0.40 0.18
Comparison of means
significant difference yes no no no no yes
p 0.05 0.07 0.3 0.07 0.09 0.002
144
3.1.4.4 Fiducial marker tests
The distance between the fiducial marker pairs corresponding to the imaged fiducial
marker positions on the various registered CT and PET images of the phantom is
shown in Table 3-10. These results demonstrate a difference in localisation of the
centre of the marker on the PET image relative to the CT image ranging from 0.09
cm to 0.69 cm. The average localisation differences are equivalent to the magnitude
of the voxel dimensions of the PET image (0.4 x 0.4 x 0.4 cm).
Table 3-10 The difference in fiducial marker localisation on registered CT and PET AC images of the phantom
Series 1 Distance between pairs Mean
dist between points
Series 3 Distance between pairs Mean
dist between points Rt marker Lt marker Rt marker Lt marker
Scan no
a 0.09 0.52 0.3
Scan no
a 0.16 0.69 0.42
b 0.11 0.3 0.2 b 0.52 0.5 0.51
c 0.19 0.27 0.23 c 0.4 0.5 0.45
d 0.64 0.58 0.61 d 0.65 0.37 0.5
e 0.44 0.33 0.39 e 0.66 0.5 0.58
f 0.09 0.32 0.2 f 0.69 0.5 0.59
mean 0.26 0.39 0.32 mean 0.51 0.51 0.51
stv 0.23 0.13 0.16 stv 0.20 0.10 0.07
Series 4 Distance between pairs Mean
dist between points
Series 5 Distance between pairs Mean
dist between points Rt marker Lt marker Rt marker Lt marker
Scan no
a 0.44 0.39 0.42
Scan no
a 0.39 0.48 0.43
b 0.33 0.36 0.34 b 0.43 0.48 0.45
c 0.39 0.39 0.39 c 0.39 0.48 0.43
d 0.39 0.39 0.39 d 0.3 0.39 0.35
e 0.39 0.61 0.5 e 0.3 0.43 0.36
f 0.39 0.36 0.38 f 0.39 0.43 0.41
mean 0.39 0.42 0.40 mean 0.37 0.45 0.41
stv 0.03 0.10 0.05 stv 0.05 0.04 0.04
Series 7 Distance between pairs Mean
dist between points
Series 8 Distance between pairs Mean
dist between points Rt marker Lt marker Rt marker Lt marker
Scan no
a 0.37 0.41 0.39
Scan no
a 0.64 0.5 0.57
b 0.33 0.41 0.37 b 0.33 0.43 0.38
c 0.37 0.3 0.34 c 0.33 0.5 0.42
d 0.33 0.3 0.32 d 0.31 0.42 0.37
e 0.33 0.32 0.32 e 0.31 0.42 0.37
f 0.37 0.4 0.39 f
mean 0.35 0.36 0.36 mean 0.38 0.45 0.42
stv 0.02 0.06 0.03 stv 0.14 0.04 0.09
145
3.1.5 Discussion and conclusions
Direct measures of accuracy for the CC and MI algorithms were able to be achieved
by registering the same image with itself (i.e. the same CT or the same PET AC
image of the phantom). Registering two sequentially acquired PET AC images with
identical phantom conditions tested the effects of intra-subject differences on
registration accuracy.
The algorithm tests (see Table 3-5) demonstrated sub-voxel levels of accuracy for the
post-registration translation parameters and less than 1° for the rotational parameters
for both the CC and MI algorithms. The differences in the voxel sizes between the
CT and PET images did not affect the registration accuracy of the algorithms.
However accuracy was slightly worse in the z-translation and the x-rotation axes
independent of algorithm and the images registered. When the voxel dimensions of
the PET images are considered (i.e. the voxel axes dimensions are equal at 0.4 x 0.4
x 0.4 cm3 for both the AC emission and transmission images), the higher levels of
inaccuracy for z-translation and x-rotation registration parameters may be due to the
search optimisation processes of the registration software rather than image voxel
size as has been suggested94, 95
.
The z-translation and x-rotation are both relative to the superior-inferior direction of
the patient which is perpendicular to the axial (or transverse) image acquisition plane
of both the CT and PET images. The order in which the translation and rotation
parameters are optimised with respect to the 3 Cartesian axes of the images can
affect optimisation robustness88
. The software search optimisation process may
place priority on optimising the in-plane parameters (the x and y-axes translations
and the z-axis rotation as demonstrated in Figure 1-11) to find the capture range (i.e.
true registration).
While the MI algorithm is recommended for registering PET and CT images88, 89,
using the CC algorithm to register copies of the same CT and PET and two PET
images of same phantom conditions provided a baseline to assess the effects of the
registration software’s accuracy. The results for the CC algorithm registrations of
identical images were expected, demonstrating that direct greyscale matching for
146
each voxel of the images should result in perfect registration81. Accuracy was
slightly worse for the registration of the two PET AC images with identical phantom
conditions for both the CC and MI algorithms. This is mostly likely due to the
differences in voxel intensity levels resulting from decay of the 18
F-FDG activity
between the acquisition of the two images.
Initial alignment of the two images > 4 cm and > 3 cm from the capture range for the
CC and MI algorithms respectively, can result in large registration errors. Different
pre-registration image alignments resulted in different registration outcomes with
relatively small rotational pre-registration offsets affecting image registration
accuracy more than translation offsets. For example a +1 cm x-translation offset will
misalign the entire secondary image by 1 cm to the left of the primary image. A +1°
x-axis rotational offset will result in a 1.7 mm anterior displacement of the secondary
image relative the primary image 10 cm from the pivotal point of rotation of the
secondary image. Despite the smaller initial alignment offset between the two
images which a 1° rotation offset produces compared to a 1 cm translation offset, the
results of the algorithm tests demonstrated that the processes utilised by the image
registration software are more sensitive to pre-registration rotation offsets than
translation offsets.
Comparisons between AC scan-based and transmission scan-based registrations of
the phantom CT and PET images using the MI algorithm were made for two reasons.
The MI algorithm is recommended for more robust inter-modality registration86, 88
and several studies36, 90, 96
have suggested that transmission scan-based registration is
more robust than AC scan-based registration. Based on the repeatability coefficients
and comparison of the means for the different post-registration parameters, the
transmission scan-based registration technique was found to have the higher level of
accuracy and reproducibility, across multiple patient specific conditions, including
respiration. The mean and standard deviations of the post-registration parameters for
the transmission scan-based registrations relative to the baseline registrations were
0.22 ± 0.21, 0.13 ± 0.12, 0.21 ± 0.15 cm for the x, y and z translation parameters
respectively. Differences to the baseline registrations of 0.40 ± 0.43, 0.59 ± 0.44,
and 0.37 ± 0.47° were achieved for the x, y and z rotation parameters respectively.
147
The AC and transmission scan-based registration parameters were compared with
baseline registration parameters as a means of evaluating the two different automatic
registration techniques. This approach has been applied in numerous image
registration analysis studies85, 86, 90, 94, 96
. The baseline registrations of the CT and
PET images were performed manually using visual assessment of the alignment of
different features of the phantom to validate the accuracy of thee registrations.
While it can be argued that these baseline registrations cannot been seen as a direct
measure of accuracy, the phantom had clearly defined regular surfaces which made
visual assessment of the registration of the CT and PET images relatively easy.
The differences in the imaged dimensions of the moving sphere between the PET and
CT images did not affect the level of accuracy or reproducibility of the transmission
scan-based registrations. The voxels representing the imaged moving sphere make
up a relatively small percentage of the total voxels in the CT and PET images of the
phantom. Therefore differences between the CT and PET images due to the moving
sphere will not significantly impact on the evaluation of the joint entropy of the two
images when they are registered using the MI algorithm. While the imaged volumes
of the moving sphere on the free-breathing CT scans varied, the mean centre of the
imaged sphere derived using the data from all 20 free breathing CT scans, was found
to be only 2 mm inferior of the true centre of the moving sphere (see Figure 2-29).
This may also be a contributing factor as to why the motion of the sphere did not
affect registration outcomes. The fact that the repeatability coefficients and
comparisons of the means showed improved registration outcomes for the AC scan-
based registrations where the sphere was moving is due to large errors for the
registration of some of the PET series 1 and 3 images. These outliers are result of
registering CT and PET images of the phantom with no water or activity in the main
tank.
The fiducial marker tests demonstrated localisation errors of the centre of the
markers on the PET images were equivalent to PET image voxel dimensions. The
imaged size of the fiducial markers on the PET images was larger than the actual
dimensions of the markers due to partial volume effects. Point-based CT-PET
registration using fiducial markers could actually introduce registration errors larger
those demonstrated using the automatic voxel-based techniques. The phantom image
148
registration tests performed in this study do not support using fiducial markers as a
gold standard to assess registration outcomes as other studies have done82, 98. This
and the extra radiation exposure for nuclear medicine technologists from injecting
the 18F-FDG into the markers indicate that there is no advantage in using fiducial
markers in the CT-PET fusion process.
149
3.2 Clinical trial of the image registration protocol
3.2.1 Aims
The aims of the patient image registration trials are to:
1. Perform a pilot study which will
• Combine the techniques tested in chapters 2 and 3 for image manipulation
and registration which will form the basis of the protocols to be used by
the radiation therapists in the patient data image registration trials.
• Determine baseline registration outcomes to be used to analyse the
radiation therapist image registration results.
2. Determine the level of reproducibility of the manual registration results of 12
radiation therapists registering the 9 patient planning CT and PET AC scans
acquired.
3. Determine the level of reproducibility of the automatic algorithm-based
registration results performed by 12 radiation therapists using two different
automatic registration protocols based on the phantom registration tests to
register the image data sets of the 9 patients. The automatic registration
protocols would involve:
• Registering the planning CT scan with the PET AC scan using the MI
algorithm, or
• Registering the planning CT scan with the PET transmission scan first to
use as the basis for registering the PET AC scan with the planning CT
scan.
150
3.2.2 Methodology
3.2.2.1 Pilot study and RT training
A pilot study to determine baseline registration results for each patient’s CT and PET
image data sets was conducted prior to the image registration trials. Two RTs were
involved in the pilot study; the principal researcher of this project and a senior RT in
charge of a treatment planning department. Neither of these two RTs participated in
the image registration trials. The methodology used in the pilot study was as follows:
Pilot Study
1. As the planning CT and PET image data sets were acquired for each patient they
were imported into Pinnacle3
2. The data sets were then registered by one of the RTs using the Syntegra software.
The techniques tested previously for image manipulation and registration were
incorporated into the pilot study including:
.
• The window widths and levels determined from the phantom tests and
confirmed using the patient PET scans in Chapter 2.
• The pre-registration importation and alignment of images.
3. The registration results for each patient were evaluated by the other RT.
4. If there was a difference of opinion, both RTs reviewed the registration results
again and adjustments to the registration of the images were made if necessary.
5. The final registration of each patient’s CT and PET AC images provided the
baseline registrations which would be used to analyse the RT image registration
trial results.
The image registration evaluation criteria used in this study were based on matching
anatomical features that could be seen on both the CT and PET AC scans (See Figure
3-7). The criteria were as follows:
• Alignment of the vertebral bodies and sternum (Figure 3-7c).
• Alignment of air-tissue interfaces in stable regions of the patient as shown
in Figures 3-7a and 3-7b (i.e. excluding the diaphragm, and lower lobe
regions of the lungs).
151
• Alignment of any hypo or hyper-intense anatomical features of the PET
AC scan with corresponding features on the planning CT, such as the left
ventricle of the heart, the liver or kidneys (see Figures 3-7a and 3-7b).
Figure 3-7 Anatomy-based matching criteria for the baseline registrations of the patient CT and PET data sets
(a)
(b)
(c)
152
Radiation therapists interested in participating in the trial were invited to an
information session which provided background information relating to the research.
Once the RTs who were going to participate in the trials had signed the participant
information and consent form, they were given a training session on PET image
interpretation and the principles of image registration. None of the participating RTs
participating in the image registration trials had any clinical experience in registering
CT and PET images or using the Syntegra software. Each participant was required to
attend a “hands-on” session to familiarise them with using the Syntegra software and
to give them a dummy run using the prescribed methodology for the trial. The RTs
were provided with the image registration evaluation criteria used in the pilot study
and asked to apply it to a patient data set they had just registered using various
visualisation tools in the Syntegra software.
RT training
3.2.2.2 RT image registration trials using patient data
The patient image data was prepared for the RTs to register in the trials. Two plans
were created for each patient, one for the manual and one for the automatic
registrations to be performed. The CT and PET image data acquired for the study
was imported for each plan. For patients 1, 3, 6 and 7 only the PET AC scan was
collected and hence imported, whereas both the PET AC and transmission scans
were able to be imported for the remaining patients (2, 4, 5, 8 and 9). The position of
the image data sets in these plans were not adjusted from their original DICOM
coordinates nor were any of the optimal window widths or levels applied for viewing
the PET.
Identical copies of the pre-registration plans were made for each RT participating in
the trial. Each RT performed the required image registrations for all 9 patients over a
2 week period, with two RTs at a time completing the trial. During the first week for
each pair of RTs, one RT was given patients 1-5 to register while the other RT was
given patients 5-9 to register. In the second week each RT was given the reverse set
of patients.
153
The resources used in the training sessions were made available for the RTs to
consult at anytime during the trial. A set of instructions, detailing the methodology
to be used for the registration trials was supplied to the RTs (see Appendix 4 for a
copy of these instructions). The RTs were instructed to:
1. Use the optimal window widths and levels for viewing the PET images that had
been determined previously.
2. First register the patient images manually using the CT and PET AC scans.
3. Prior to performing the automated registrations the RTs were required to pre-
align the images so that the lungs on both the CT and the PET scans were
roughly overlaid.
4. Register the patient images automatically using the:
• CT and PET AC scans (patients 1, 3, 6 and 7 ) or
• CT, PET transmission and PET emission scans (patients 2, 4, 5, 8 and 9),
registering the planning CT scan with the PET transmission scan first,
then registering the PET AC scan with the planning CT based on the CT-
PET transmission scan registration results.
5. The RTs were not allowed to adjust the results of the automated registrations.
6. The RTs were requested to not compare their registration results with the other
RTs participating in the trial.
154
3.2.3 Data analysis
The results of the RT registrations of the patient planning CT and PET images used
the same methods as those used in analysing the phantom registration tests.
The post-registration parameters for each of the RT registrations of the patient CT
and PET images were subtracted from the baseline registration of each patient’s
images.
Calculation of the post-registration results relative to the baseline registrations
Box plots of the post-registration translation and rotation parameters relative to the
baseline registrations were created for:
Box plots of the post-registration results
• The manual registration results for each patient
• The automated registration results for each patient
• The combined manual registrations for all patients
• The combined AC scan-based automated registration results (patients 1,
3, 6 and 7)
• The combined transmission scan-based automated registration results
(patients 2, 4, 5, 8 and 9)
Scatter plots of the standard deviation versus the mean difference from the baseline
registrations was created for:
Plots of the standard deviation against the mean of the registration results
• The post-registration x, y, and z translations and the post-registration x, y,
and z rotations for:
The combined manual registration results
The order that the RTs manually performed the manual
registrations in (i.e. the first and second group of patients given to
each RT to register)
• The post-registration x, y, and z translations and the post-registration x, y,
and z rotations for:
The combined automated registration results
155
The combined AC scan-based automated registration results
(patients 1, 3, 6 and 7)
The combined transmission scan-based automated registration
results (patients 2, 4, 5, 8 and 9)
The order that the RTs performed the AC scan-based automated
registrations in (patients 1, 3, 6 and 7)
The order that the RTs performed the transmission scan-based
automated registrations in (patients 2, 4, 5, 8 and 9)
The repeatability coefficient (refer to Section 3.1.3.2) was calculated for each of the
6 different registration parameters. The results were grouped by the registration
technique to calculate within-subject variance to determine which of the methods
was more reproducible. The results for each registration technique were then
separated according to the order that the patients were registered by the RTs to
calculate within-variance to identify if the registration results were more
reproducible as the RTs gained experience.
Calculation of the repeatability coefficient
The RT registration results for different patients were combined. The mean and
standard deviation was calculated using the absolute values for each of the post-
registration translation and rotation parameters. A paired t-test was used to compare
the means for each of the post-registration parameters between the manual and
automated registration methods. To compare the AC scan and transmission scan-
based registration results a t-test was performed.
Comparison of the manual and automated mean registration results
156
3.2.4 Results
3.2.4.1 Pilot study and RT training
Baseline registrations were achieved for each of the patient planning CT scans and
their PET AC images. It was found that there appeared to be slight differences in
patient dimensions and position between the CT and PET images. These differences
were of the order seen in daily patient positioning during radiotherapy treatment
(such as chin position as shown in Figure 3-8). During the pilot study it was found
that the lung surfaces on both data sets needed to be roughly aligned prior to running
the automatic registration software using the MI algorithm. If the surface of the
lungs were close to either the first or last slice of the CT image then misregistration
would often occur (see Figure 3-8).
Figure 3-8 Patient positioning and misregistration issues noted during the pilot study The image on the left demonstrates the slight difference in patient chin position between the CT and PET images. The image on the right demonstrates the misregistration error that could occur if the surface of the lungs were close to either the first or last slice of the CT image
Initially most of the RTs expressed concerns relating to their lack of experience in
viewing and registering PET images, feeling that this would produce poor
registration results. They all felt more confident after participating in the training
session and as they registered more of the patient data sets during the trial.
157
3.2.4.2 RT image registration trials using patient data
Appendix 5 contains the box plots of the 12 RT’s results for each patient for both the
manual and the automated registration techniques. These graphs demonstrate that for
each patient there is a marked reduction in the spread of the automated RT
registration outcomes (relative to the baseline registrations) when compared to the
manual RT registration results. Generally the median for the translation post-
registration parameters are similar between the manual and automated registrations.
There are however rotation parameter differences greater than 1 cm or 1° for the
medians between the registration techniques in 6 out of the 9 patients.
Figure 3-9 contains the box plots of the combined RT registration results for each
parameter from all 9 patients for the different registration techniques. From these
graphs it can be seen that over the 9 patients that:
• The magnitude of any outliers is significantly reduced for the automated
registration techniques.
• There is a narrower distribution of the automated registration results
compared to the manual registration results.
• There is a similar distribution of results when the two automated
registrations techniques are compared.
• The medians between the manual and automated techniques are similar
for the translation parameters while more variation is seen in the rotation
parameters with approximately 0.75° difference can be seen for the y and
z rotation parameters.
158
Figure 3-9 Combined RT results of the patient CT and PET images by registration technique For the manual registrations, n=108 registrations for each parameter. For the AC scan-based registrations, n=48 registrations. For the transmission scan-based registrations, n=60 registrations.
159
Figure 3-10 contains the plots of the standard deviation versus the mean of the post-
registration parameters for the RTs results relative to the baseline registrations.
These graphs demonstrate that:
• There isn’t an apparent systematic error for either the manual or
automated registration results (i.e. as the mean difference from the
baseline registrations increases the standard deviation does not increase).
• The magnitudes of the standard deviations of the different post-
registration parameters indicate that the automated registrations were
more reproducible than the manual registrations.
Figure 3-10 The standard deviation plotted against the mean for the post registration parameters: automated and manual RT registration results There are n=12 registrations per data point.
Combined manual registrations
0
0.5
1
1.5
2
2.5
3
3.5
4
-5 -4 -3 -2 -1 0 1 2 3mean difference (cm)
stan
dar
d d
evia
tion
(cm
) x transy transz trans
Combined manual registrations
0
0.5
1
1.5
2
2.5
3
3.5
4
-5 -4 -3 -2 -1 0 1 2 3mean difference (degrees)
stan
dar
d d
evia
tion
(d
egre
es)
x roty rotz rot
Combined auto registrations
0
0.5
1
1.5
2
2.5
3
3.5
4
-5 -4 -3 -2 -1 0 1 2 3mean difference (cm)
stan
dar
d d
evia
tio
n (
cm)
x transy transz trans
Combined auto registrations
0
0.5
1
1.5
2
2.5
3
3.5
4
-5 -4 -3 -2 -1 0 1 2 3mean difference
stan
dar
d d
evia
tio
n
(deg
rees
)
x roty rotz rot
Plots of the standard deviation versus the mean of the RT automated registration
results separated into AC scan-based or transmission scan-based registrations are
shown in Figure 3-11. While these graphs do not demonstrate any systematic error
for either of the automated registration techniques there appears to be no apparent
difference in reproducibility between the two techniques when the magnitude of the
standard deviations are compared.
160
Figure 3-11 The standard deviation plotted against the mean for the post registration parameters: AC scan and transmission scan-based automated RT registration results There are n=12 registrations per data point.
AC scan registrations
0.0
0.2
0.4
0.6
0.8
1.0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5mean difference (cm)
stan
dar
d d
evia
tio
n (
cm)
x transy transz trans
AC scan registrations
0.0
0.2
0.4
0.6
0.8
1.0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5mean difference (degrees)
stan
dard
dev
iatio
n (d
egre
es)
x roty rotz rot
Transmission scan registrations
0.0
0.2
0.4
0.6
0.8
1.0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5mean difference (cm)
stan
dar
d d
evia
tio
n(c
m)
x transy transz trans
Transmission scan registrations
0.0
0.2
0.4
0.6
0.8
1.0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5mean difference
stan
dar
d d
evia
tio
n
(deg
rees
)
x roty rotz rot
The repeatability coefficients calculated for the combined the manual and automated
RT registrations are markedly different (see Table 3-11) and show agreement with
the plots of the standard deviation versus the mean in Figure 3-10. On average the
automated RT registration results were 4 times more reproducible than their manual
registration results. When the automated registration results are separated into AC
scan-based or transmission scan-based registrations, the repeatability coefficients
indicate a difference of 0.1 cm or 0.1°. This demonstrates no difference in
reproducibility between the two automated registration techniques.
161
Table 3-11 Repeatability coefficients for the manual and automated RT registration results
Registration technique Repeatability coefficient
x trans (cm)
y trans (cm)
z trans (cm)
x rot (degrees)
y rot (degrees)
z rot (degrees)
Combined RT manual registrations for patients 1-9
1.284 1.411 2.197 4.296 3.413 5.008
Combined RT auto registrations for patients 1-9
0.246 0.318 0.539 1.075 0.886 0.771
RT auto registrations: PET AC scan-based registration
0.187 0.221 0.703 0.901 0.752 0.671
RT auto registrations: PET trans scan-based registration
0.222 0.260 0.833 0.886 0.817 0.760
Comparison of the means of the registration results for the manual and automated
registration results demonstrate that the means of the absolute values for the different
post-registration parameters are smaller for the automated registrations compared
those for the manual registrations (see Table 3-12). Paired t-tests of the means for
the different post-registration parameters demonstrate that means are significantly
different for 5 out of the 6 parameters (p < 0.05).
Table 3-12 Paired t-test comparisons of the means of the registration parameters for the RT manual and automated registrations
x trans (cm)
y trans (cm)
z trans (cm)
x rot (degrees)
y rot (degrees)
z rot (degrees)
Manual registrations
mean 0.39 0.46 0.58 1.33 1.31 1.35
standard deviation 0.37 0.39 0.71 1.04 1.43 1.30
Automated registrations
mean 0.15 0.37 0.30 1.12 0.69 0.50
standard deviation 0.11 0.39 0.22 0.89 0.69 0.58
Comparison of means
significant difference yes yes yes no yes yes
p <0.001 0.03 <0.001 0.12 <0.001 <0.001
162
Comparison of the means of the registration results for the RT AC scan-based and
transmission scan-based registrations indicates that there is no overall difference
between the levels of accuracy for these two automated registration techniques. The
means of the absolute values for the different post-registration parameters are smaller
for the AC scan-based registrations compared those for the transmission scan-based
registrations (see Table 3-13). However t-tests of the means for the different post-
registration parameters demonstrate that means are not significantly different for 4
out of the 6 parameters.
Table 3-13 t-test comparisons of the means of the registration parameters for the RT AC and transmission scan-based registrations
x trans (cm)
y trans (cm)
z trans (cm)
x rot (degrees)
y rot (degrees)
z rot (degrees)
AC-based registrations
mean 0.12 0.34 0.27 1.16 0.41 0.35
standard deviation 0.07 0.30 0.25 1.08 0.29 0.19
Trans-based registrations
mean 0.17 0.38 0.33 1.09 0.91 0.62
standard deviation 0.14 0.46 0.20 0.72 0.83 0.74
Comparison of means
significant difference no no no no yes yes
p 0.05 0.6 0.2 0.7 <0.001 0.02
163
Figures 3-12 and 3-13 contain the plots of the standard deviation versus the mean by
order of registration for the manual and automated registration results. When the
manual and automated registration results of the RTs are grouped in the order that
the patient image data was supplied for registration, there is an improvement in the
reproducibility of the manual registration results in the second group of patients
registered, but not for the automated registrations. Improvement is demonstrated for
the rotation parameters only for the second group of manually registered patient
images.
Figure 3-12 The standard deviation plotted against the mean for the post registration parameters: manual RT results based on the order the registrations were performed There are n=6 registrations per point.
1st group of patients
0
0.5
1
1.5
2
2.5
3
3.5
4
-5 -4 -3 -2 -1 0 1 2 3mean difference (cm)
stan
dar
d d
evia
tion
(cm
) x transy transz trans
1st group of patients
0
0.5
1
1.5
2
2.5
3
3.5
4
-5 -4 -3 -2 -1 0 1 2 3mean difference (degrees)
stan
dar
d d
evia
tion
(d
egre
es)
x roty rotz rot
2nd group of patients
0
0.5
1
1.5
2
2.5
3
3.5
4
-5 -4 -3 -2 -1 0 1 2 3mean difference (cm)
stan
dar
d d
evia
tion
(cm
) x transy transz trans
2nd group of patients
0
0.5
1
1.5
2
2.5
3
3.5
4
-5 -4 -3 -2 -1 0 1 2 3
mean difference (degree)
stan
dar
d d
evia
tion
(d
egre
e)
x roty rotz rot
164
Figure 3-13 The standard deviation plotted against the mean for the post registration parameters: automated RT results based on the order the registrations were performed There are n=6 registrations for each point.
1st group of patients: AC registrations
0.0
0.2
0.4
0.6
0.8
1.0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5mean difference (cm)
stan
dar
d d
evia
tio
n (
cm)
x transy transz trans
1st group of patients: AC registrations
0.0
0.2
0.4
0.6
0.8
1.0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5mean difference (degrees)
stan
dar
d d
evia
tio
n
(deg
rees
)
x roty rotz rot
2nd group of patients: AC registrations
0.0
0.2
0.4
0.6
0.8
1.0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5mean difference (cm)
stan
dar
d d
evia
tio
n (
cm)
x transy transz trans
2nd group of patients: AC registrations
0.0
0.2
0.4
0.6
0.8
1.0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5mean difference (degrees)
stan
dar
d d
evia
tio
n
(deg
rees
)
x roty rotz rot
1st group of patients: Transmission scan registrations
0.0
0.2
0.4
0.6
0.8
1.0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5mean difference (cm)
stan
dar
d d
evia
tio
n (
cm)
x transy transz trans
1st group of patients: Transmission scan registrations
0.0
0.2
0.4
0.6
0.8
1.0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5mean difference (degrees)
stan
dar
d d
evia
tio
n
(deg
rees
)
x roty rotz rot
2nd group of patients: Transmission scan registrations
0.0
0.2
0.4
0.6
0.8
1.0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5mean difference (cm)
stan
dar
d d
evia
tio
n (
cm)
x transy transz trans
2nd group of patients: Transmission scan registrations
0.0
0.2
0.4
0.6
0.8
1.0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5mean difference (degrees)
stan
dar
d d
evia
tio
n
(deg
rees
)
x roty rotz rot
165
The repeatability coefficients in Table 3-14 demonstrate a reduction in coefficients
for the second group of manually registered patient images of:
• 1.5° for the x rotation parameter
• 0.6° for the y rotation parameter
• 0.8° for the z rotation parameter
Generally the repeatability coefficients are similar for the automated registration
techniques for the first and second group of registered patients. There are some
differences between the coefficients for the first and second groups of registered
patients. There are:
• A reduction of 0.5 cm for the z translation coefficient for the second
group of registered patients using the AC scan-based registration
technique.
• An increase of 0.04 cm for the x rotation coefficient for the second group
of registered patients using the AC scan-based registration technique.
• An increase of 0.04 cm for the z rotation coefficient for the second group
of registered patients using the transmission scan-based registration
technique.
Table 3-14 Repeatability coefficients based on the order that the RT performed the registrations
Registration technique and order performed
Repeatability coefficient x trans
(cm) y trans
(cm) z trans (cm)
x rot (degrees)
y rot (degrees)
z rot (degrees)
1st1.362 group of patients given to RTs
to manually register 1.443 2.230 5.059 3.743 5.452
2nd1.304 group of patients given to RTs
to manually register 1.350 2.188 3.541 3.152 4.657
1st
0.207 group of patients given to RTs
to automatically register: PET AC scan-based registration
0.163 0.926 0.666 0.747 0.780
2nd
0.144 group of patients given to RTs
to automatically register: PET AC scan-based registration
0.256 0.449 1.026 0.766 0.567
1st
0.207 group of patients given to RTs
to automatically register: PET trans scan-based registration
0.301 0.337 1.227 0.933 0.612
2nd
0.340 group of patients to RTs to
automatically register: PET trans scan-based registration
0.434 0.363 1.141 0.920 1.026
166
3.2.5 Discussion and conclusions
The clinical trial of the image registration protocols based on outcomes of the
phantom tests, demonstrated that the automatic registration of the patient CT and
PET images were more accurate and reproducible than the manual registrations. The
mean and standard deviations of the post-registration parameters for the automatic
registrations relative to the baseline registrations were 0.15 ± 0.11, 0.37 ± 0.39, 0.30
± 0.22 cm for the x, y and z translation parameters respectively. Differences to the
baseline registrations of 1.12 ± 0.89, 0.69 ± 0.69, and 0.50 ± 0.58° were achieved for
the x, y and z rotation parameters respectively.
Unlike the phantom registration results, there was no significant difference in the
reproducibility or the level of accuracy of the AC scan and the transmission scan-
based registrations of the patient images. This may be the result of differences
between the simulated phantom conditions and patient physiological changes. It was
noted that there were large registration errors for some of the phantom AC scan-
based registrations where the main tank of the phantom was empty. These outliers
would have increased the mean post-registration parameters relative to the baseline
registration parameters. The effects of daily patient changes in dimensions and
respiration effects on the whole chest region may result in differences between the
dimensions of the AC and transmission scans which would not have occurred in the
images of the main tank of the phantom.
While the manual RT registrations of the patient images were less accurate and
reproducible than their automatic registration results, when the manual registration
results were grouped in the order the registration were performed by the RTs, their
results improved. This is most likely due to the RTs gaining more experience
assessing the alignment of different anatomical features on the patient CT and PET
images. The clinical experience of the RTs who participated in the trials range from
1 – 20 years, however none of them had previous experience with registering PET
images or using the Syntegra software. As there was no improvement in the
automatic registration results with experience, the training session prior to
participation in the image registration trials most likely reduced bias in the
registration results due to lack of familiarity with the software. Rousson et al’s121
167
review of methods for assessing intra and inter-observer, and test-retest
reproducibility, highlights the importance of training to achieve higher levels of
reliability for different techniques.
As the phantom demonstrated that initial alignment of the two images should within
3 cm from the capture range (i.e. true registration) for the MI algorithm to avoid
large registration errors, the RTs were instructed to roughly align the images prior to
executing the automatic registration software. The pilot study for the image
registration trials also highlighted the importance of reasonably aligning the images
prior to registration due to the truncation of the planning CT scans of the patient
producing registrations errors86. The top and bottom slices of the planning CT scans
can be interpreted as local minima which can be seen as correctly aligned with the
surface of the patient’s lungs on the PET image by the MI algorithm81
.
Assessment of the RT registrations of the patient images used the same approach as
that used for assessing the automatic registrations of the phantom images. Baseline
registrations were achieved via manual alignment of the patient CT and PET images
using visual assessment of the alignment of different patient features to validate the
registration. Again, it can be argued that these baseline registrations cannot be seen
as a direct measure of accuracy. The use of experienced RTs to visually verify the
baseline registrations used to assess automated registration outcomes of patient
images is supported in the literature90, 94, 98
.
168
4
4.1 Phantom contouring tests
GTV delineation technique evaluation and protocol
development for the clinical trial
4.1.1 Aims
To investigate if a threshold contouring technique for PET images can be applied in
the radiotherapy treatment planning environment. Various threshold values applied
to a semi-automated contouring technique will be used to generate 3D contours of
regions of interest on registered CT and PET AC images of the phantom CT and
PET. The resulting contours will be assessed to determine threshold values that
define geometrically accurate regions of interest. The effect of the following PET
image characteristics on selecting appropriate threshold values will be examined:
1. The effect of different concentrations of 18-FDG uptake in a region of interest.
2. The effect of increasing background activity relative to a region of interest.
3. The effect of a region of interest under the influence of motion.
4. Using pixel versus SUV data.
169
4.1.2 Methodology
4.1.2.1 Threshold value contouring of phantom PET AC data
The baseline registered pairs of CT and PET AC images of the phantom (see Table
3.4 in Chapter 3), were used to perform the threshold contouring tests. On each of
the PET AC scans used in this series of registered images, the central rods of the
main tank and the static or moving sphere were contoured using various threshold
values. The results of the image quantification of the phantom PET scans in Chapter
2 provided the maximum pixel and SUV data for each of the features of the phantom,
from which different threshold values (percentages of the maximum value) were
calculated.
The technique that was used to determine each contour was as follows (using the
PET AC scan 4a of the phantom as an example):
1. A given percentage of this maximum value was calculated based on the
maximum pixel value of 2414 for the 0.5 cm central rod in this image. For
example the 20% threshold value for the 0.5 cm rod on this AC image was 0.2 x
2414 = 483
2. A semi-automated contouring technique that edge detects voxels within a
specified range of on a specified image data set was used. This involves:
• Creating a new contour and associating it with the PET AC image data
set.
• Specifying a minimum (483) and maximum (kept as the maximum pixel
number of the image) voxel pixel value range that that is to be included in
the contour (see Figure 4-1).
• The auto-contour tool that performs edge detection throughout all the
slices of the image data set was selected and the curser was placed on the
image to indicate where the edge detection was to begin.
3. A range of threshold values were used to create a series of contours of the 0.5 cm
diameter rod on the PET scan using the semi-automated contouring technique.
4. The volume of each contour generated from the different threshold values was
recorded.
170
5. The minimum SUV value of each contour was determined using the ROI
statistics tools in the TPS and recorded.
Figure 4-1 Creation of the 20% threshold contour for the 0.5 cm rod using the semi-automated contouring technique
4.1.2.2 Verification of the geometrical accuracy of the threshold values
For each of the central rod and static sphere contours, the following technique was
used to determine which of the threshold values resulted in a contour that accurately
defined the geometric edge of the different components of the phantom on each PET
AC scan:
1. The CT scan that each PET AC scan had been registered with was used as a
visual template. Each CT scan was displayed with a mediastinal window width
and level so that the water inside each compartment could be differentiated from
the Perspex.
2. The PET AC was displayed with the window width and level that best
demonstrated regions of intense uptake.
171
3. Rod and static sphere contour verification:
• The contour which followed the inside of each rod and the static sphere
on the CT scan and conformed to the shape of the corresponding region of
intense uptake on the PET AC scan was identified (see Figures 4-2a and
4-2b).
4. Moving sphere contour verification:
• The 4D volume contour of the moving sphere was used as the template
instead of the imaged CT data. The 3D model of inner perspex
dimensions of the moving sphere was positioned on each CT scan at the
known central coordinates of the static sphere position relative to the
main tank.
• The contour which followed the shape of the 3D model of the inner
dimensions of the moving sphere and conformed to the shape of the
corresponding region of intense uptake on the PET AC scan was
identified (see Figure 4-2c).
5. An important selection criterion for any contour was that no part of the line of the
contour went inside the inner edge of perspex that defined each compartment (as
visualised on each CT scan).
6. The threshold value identified as resulting in a geometrically accurately contour
was recorded.
172
Figure 4-2 Verification of the geometrical accuracy of the threshold values Visual verification using the CT images of the phantom to determine the geometrical accuracy of the contours generated using different threshold values for (a) the central rods and (b) the static sphere. The 4D volume contour of the moving sphere was used to verify the accuracy of the contours for the moving sphere as shown in (c)
(a)
(b)
(c)
173
4.1.3 Data analysis
There were 6 PET AC images for each phantom condition and each of these images
was contoured using a range of threshold values. For each AC image, the threshold
value that was observed to be a geometrical match for the various contoured
components of the phantom was noted. The threshold value which most frequently
resulted in a visual match with the CT image of the phantom was considered to be
the most appropriate value to produce an accurate contour for a particular phantom
component for each PET series of AC images.
Verification of the geometrical accuracy of the threshold values
The most frequently verified threshold value for each central rod (grouped by the
background-activity ratios of the main tank to the central rod or sphere) was plotted
against central rod diameter to determine any relationship between threshold values
and background activity.
The mean volume of each verified threshold value was calculated for each
component of the phantom for each of the PET series using the contour volume data
of the 6 AC images within each series. This mean volume was compared to the
baseline CT and PET 3D MBS generated contours of the phantom performed in
Chapter 2.
Comparison of pixel-based and SUV-based threshold values
The minimum and maximum SUV values contained within each threshold-generated
contour were used to determine the percentage maximum threshold value for the
SUV data. The resultant SUV-based percentage maximum threshold values were
compared to the pixel-based values used to generate the contour.
174
4.1.4 Results
4.1.4.1 Threshold value contouring of phantom PET AC data
The threshold values that resulted in accurate definition of each phantom component
for the different PET scan series are shown in Table 4-1.
Table 4-1 Threshold values verified for GTV definition by visual match with CT
Phantom component PET scan series and their % background activity in main tank
1 (0%)
2 (0%)
3 (0%)
4 (0%)
5 (5%)
6 (10%)
7 (20%)
8 (40%)
0.5cm rod 30 30 30 30 55 70 - - 1.0cm rod 25 25 30 30 30 40 60 - 1.5cm rod 25 25 30 30 30 35 45 - 2.0cm rod 25 25 30 30 30 30 40 55 3.0cm rod 30 25 30 30 30 30 40 50
Sphere (diameter = 4cm) Sphere is surrounded by air (0% background)
Static sphere - - 30 - - - - - Moving sphere - - - 10 - 10 10 10
Each of the central rods was injected with different concentrations of 18F-FDG for
the series 2 phantom scans. For the series 1, 3 and 4 scans, the same concentration of 18
F-FDG was injected into the central rods. A comparison of the verified threshold
values for the contours of the central rods from series 2 with those verified for series
1, 3 and 4, indicates that different concentrations of activity in the central rods does
not significantly effect the threshold values to produce a geometrically accurate
contour.
The scans in series 1-4 were all acquired without any background activity in the main
tank of the phantom. The 30% threshold value most frequently produced accurate
contours for the central rods in the absence of background activity in the main tank.
It can be seen from Table 4-1 that as the background-activity ratios of the main tank
to the central rod or sphere increased (series 5 – 8), the threshold values which
produced a geometrically accurate contour also increased.
175
The 0.5 cm diameter central was not able to be accurately contoured using any
threshold value for when the main tank background activity was 20%. Only the 2.0
cm and 3.0 cm diameter central rods were able to be accurately contoured when the
main tank background activity was 40%. The graph in Figure 4-3 demonstrates the
relationship between the central rod diameter and the threshold value required for
accurate contouring as the main tank background-activity ratio increased.
Figure 4-3 Threshold values plotted against the volume of interest (central rod) diameter
0
10
20
30
40
50
60
70
80
90
100
0 0.5 1 1.5 2 2.5 3 3.5
VOI diameter (cm)
Thre
shol
d va
lue
(% V
OI m
ax)
0% background
5% background
10% background
20% background
40% background
It was also found that motion affected the threshold levels for the sphere. A 30%
threshold value accurately contoured the static sphere in the series 3 images. For the
images where the sphere was moving (series 4, 6, 7 and 8), the threshold value that
accurately contoured the 4D volume of the moving sphere was reduced to 10% (see
Table 4-1).
176
The mean volumes for the phantom components relating to the verified threshold
levels were less than the baseline CT and PET contours for all of the phantom
components except for the 0.5 cm rod (see Table 4-2).
Table 4-2 Mean volumes of the baseline contours of the phantom compared to those generated using the verified accurate threshold values (BL = baseline CT contour volumes and the intra-series PET AC mean volumes from in Chapter 2. TH = verified threshold value intra-series mean volumes)
Images 0.5cm 1.0cm 1.5cm 2.0cm 3.0cm Static Moving
BL TH BL TH BL TH BL TH BL TH BL TH BL TH
CT 4.7 - 15.7 - 33.8 - 57.1 - 126.7 - 20.3 - 40.5 -
PET Series 1 7.3 7.0 16.6 14.0 34.5 24.0 61.4 45.0 133.6 99.5 - - - -
PET Series 2 7.7 6.9 21.9 11.2 40.9 17.1 62.9 43.6 128.6 102.1 - - - -
PET Series 3 7.2 7.6 16.5 12.5 34.8 18.1 61 40.7 136.2 96.8 21.0 19.4 - -
PET Series 4 6 10.1 22.5 10.5 40.2 20.2 62.7 35.8 129.7 76.9 - - 45.5 36.8
PET Series 5 8.5 10.9 14.4 23.4 35.6 36.9 61.7 42.4 138.3 121.5 - - - -
PET Series 6 5.8 8.6 21.8 18.5 42.4 32.2 62.2 50.1 136.9 126.7 - - 45.1 28.2
PET Series 7 7.5 - 16.5 8.2 37.4 24.7 61.4 40.4 138.8 99.0 - - 45.3 23.8
PET Series 8 7.8 - 16.1 - 34.6 - 62 53.5 133.5 74.4 - - 45.4 23.1
177
The ratio of the minimum SUV value of a given component contour to the maximum
SUV of the component demonstrated that equivalent contouring results would be
achieved using the same percentage maximum threshold value based on the SUV
data as the pixel-based percentage maximum threshold value used to generate the
contours. Table 4-3 contains a sample of these results for the 1.5 cm central rod for
the PET AC image data from the series 4 scans of the phantom.
Comparison of pixel-based and SUV-based threshold values
Table 4-3 Comparison of the percentage maximum threshold value for the SUV data based on the contouring results The data in the volume of interest (VOI) column shows the maximum pixel and SUV values for the 1.5 cm central rod for the PET AC image data from the series 4 scans of the phantom. The minimum pixel values are those calculated to contour the 1.5 cm rod for a particular percentage maximum threshold value (i.e. 20% and 30%). After the contour was generated using the pixel-based threshold, the minimum SUV data was extracted from the contoured volume. The ratio of the minimum SUV value to the maximum SUV value provides the equivalent percentage maximum threshold value.
Scan no
VOI image data 20% threshold based contours 30% threshold based contours
max pixel value
max SUV
Min pixel value
used to generate
the contour
Contour min SUV
Equiv % max SUV
Min pixel value
used to generate
the contour
Contour min SUV
Equiv % max SUV
a 11770 62.9 2354 12.6 20.0% 3531 18.9 30.0%
b 9054 52.1 1810.8 10.5 20.2% 2716.2 15.9 30.5%
c 23333 144.9 4666.6 29.0 20.0% 6999.9 41.8 28.8%
d 20696 138.4 41392 27.7 20.0% 6208.8 41.6 30.1%
e 18683 135.4 3736.6 27.1 20.0% 5604.9 40.7 30.1%
f 6345 49.5 1269 9.9 20.0% 1903.5 14.9 30.1%
178
4.1.5 Discussion and conclusions
A semi-automated adaptive threshold technique was found to accurately contour the
geometric edge of the different features on the phantom PET images. This
contouring technique used threshold values based the maximum pixel value
measured within a VOI. The value of the threshold level which provides an accurate
contour of a VOI is dependant on object diameter and the background-activity ratio
of the surrounding voxels. However a 30% threshold level could be applied to
objects independent of diameter size when there was no background activity.
Comparison of pixel-based and SUV-based threshold values demonstrated that
identical contours would be created on the PET image if the same threshold values
were applied to the maximum SUV values measured within a VOI (see Table 4-3).
The plotted results of threshold variation with respect to VOI diameter and
background-activity in Figure 4-3 correlate with the plotted results of Erdi et al’s105
study which also tested an adaptive threshold contouring technique using PET
images of a phantom. Erdi et al’s105 study found that a 40% threshold value
accurately contoured objects larger than 4ml with background-activity ratios less
than 20%. Similar phantom studies by Yaremko et al122 and Okubo et al123
found
that thresholds of 35% and 40% respectively accurately contoured spheres with a
diameter > 2.0 cm.
The qualitative assessment of the PET images did not appear to image the moving
sphere over its full range of motion when the activity in the main tank relative to the
central rods and sphere increased above 20%. However, the contouring tests results
demonstrated that an accurate 4D volume of the moving sphere could be contoured
on the PET images, regardless of background activity. The threshold level which
produced an accurate 4D volume of the moving sphere was 10%. Other phantom
studies which imaged a moving sphere on a stand alone PET scanner also concluded
that an accurate 4D volume could be determined from PET images and also observed
a reduction in contouring threshold values for the moving sphere compared to static
objects70, 122. Based on the results of this study (which are supported by the results of
Caldwell et al70 and Yaremko et al122) it can be concluded that ITV margins for
moving objects are included in the contoured PET volumes using images acquired on
179
stand alone PET scanners. This has implications for the margins applied to GTVs to
derive a PTV for lung tumours. Uncertainty in localisation of the GTV dimensions
due to respiration can now be accounted for when 10% of the maximum uptake
within a tumour is used to contour a GTV on a whole-body 18
F-FDG PET image.
Okubo et al123 obtained all the images for their phantom study on a combined
CT/PET scanner and found that the imaged range of motion for a moving sphere was
smaller on the PET images than the actual sphere’s range of motion. This is due to
the faster acquisition times for the CT-based attenuation correction of the PET
emission scans on a CT/PET scanner compared to those than the transmission scans
obtained on a stand alone PET scanner124. 4D CT and PET images are required to
accurately image moving objects imaged on combined CT/PET scanners125, 126 which
Okubo et al’s123 study did not perform when imaging the moving sphere on their
phantom. From Okubo et al’s123
results it can be concluded that ITV margins are not
included in the contoured PET volumes using images acquired on combined CT/PET
scanners which have not been co-ordinated with a patient’s respiratory cycle.
Repeating the phantom imaging and contouring tests on a combined CT/PET scanner
with respiratory gated imaging protocols is recommended for future research.
The threshold values were deliberately chosen in this study so that the resulting
contours encompassed all the voxels on the PET images which visually corresponded
to the same feature on the transverse images of the registered CT scan. Okubo et
al123
commented that their 35% threshold value produced contours which were 3 mm
smaller than the actual sphere diameter in the transverse plane. It was felt that the
criteria for determining accurate contouring threshold values used in this study would
avoid under-estimates of the dimensions of contoured PET volumes. Table 4-3
indicates that the threshold method for contouring resulted in volumes systematically
lower than the baseline volumes. Closer inspection of the threshold contouring
results demonstrated that the most superior and inferior aspects of each of the
phantom components were not contoured, despite accurate contouring in the
transverse planes. This may be due to the loss of counts at the superior and inferior
edges of the phantom.
180
Despite these phantom related contouring errors, two reviews of CT/PET contouring
techniques in radiotherapy (MacManus et al127 and Nestle et al128) indicate that
adaptive thresholding techniques have demonstrated the highest levels of accuracy to
date. It was also reported by MacManus et al127 that this contouring technique has
also been found to correlate to with pathological specimens of tumour volumes. This
provides independent support for the adoption of the adaptive thresholding technique
tested and verified on the phantom images into a clinical protocol.
181
4.2 Clinical trial of the GTV delineation protocol
4.2.1 Aims
The aims of the patient GTV contouring trials are to:
1. Perform a pilot study which will
• Incorporate the results of the phantom contouring tests to form the basis
of the protocols to be used by the radiation oncologists in the patient GTV
contouring trials.
• Determine the threshold values that should be used for contouring the
GTVs on the PET AC scans of each patient.
2. Train the participating radiation oncologists to use:
• The image manipulation and contouring tools available with the image
registration software in the treatment planning system.
• The protocols and instructions for the trial.
3. Compare the outcomes of the CT only and the PET AC defined GTV results to
determine the level of reproducibility of each of the different contouring
methods.
182
4.2.2 Methodology
4.2.2.1 Pilot study and RO training
4.2.2.1.1 Pilot study
The baseline registrations of the patient CT and PET images image registration trials
were used to perform a pilot study to determine the threshold values for contouring
the GTV on the PET AC data of each patient. This was done to keep the time
required to complete the contouring process in the trials to a minimum. The
methodology for the pilot study was:
1. The following window width and levels were used to view the CT and PET
images:
• CT
o The TPS system defined Lung preset was used for viewing for
volumes in lung region or
o The TPS system defined Thorax preset for volumes and involved
nodes in the chest wall or mediastinum.
• PET (from the results of the window width and level tests in Chapter 2)
o Window = 0.2 and level = 0 for viewing of background and intense
uptake.
o Window = 0.2 and level = 0.1 for viewing intense uptake regions only
2. The maximum diameter in the transverse plane was measured for each VOI that
corresponded to the intense uptake regions identified as malignant lung cancer on
each patient’s PET AC scan (based on the radiologist’s report).
3. The same semi-automated contouring technique used for the phantom contour
tests was used to contour the GTV (primary and lymph nodes) of each patient
based on:
• The results of the image analysis of the patient PET AC images in
Chapter 2 to get the background percentage of uptake relative to the
maximum count of each of the VOIs
• The results of the phantom contouring tests for selecting an appropriate
threshold value for defining the geometric edge of a GTV on the PET AC
data.
183
4. The window width and level used for viewing intense uptake regions only on the
PET AC scans was applied post contouring to verify that the threshold level was
appropriate.
• If the final contour was much larger than the intense region of uptake then
the threshold value need to be increased
• If the contour was smaller than the intense region of uptake then the
threshold value needed to be decreased.
The MBS tools were not available on the clinical systems during the period that the
ROs would be participating in the GTV delineation trials. Hence, a technique for
determining the maximum pixel value in intense uptake regions without using the
MBS model of a sphere needed to be determined for the trials. The following
technique was tested to see if it provided equivalent maximum pixel value results:
1. Viewing the PET AC image only, the transverse slice corresponding to the
middle of the VOI (identified as the GTV or involved lymph nodes) was located
by scrolling through the images.
• The middle of the VOI was half way between the most superior and
inferior slices the VOI was imaged on.
2. A profile of the pixel values was taken through the VOI using the Pinnacle3
3. The maximum pixel value on the line profile was used to calculate the minimum
count value using the appropriate threshold level for that patient so that the semi-
automated contouring technique could be applied.
tools
and drawing a line through the VOI on the identified middle slice (see Figure 4-
4)
4.2.2.1.2 RO training
Radiation oncologists interested in participating in the GTV delineation trial were
invited to an information session which provided background information relating to
the research. As with the RTs the ROs who were going to participate in the trials had
to sign the participant information and consent form. To familiarise the ROs with the
semi-automated threshold technique for GTV definition on PET data, the principal
researcher was available during the contouring of the first patient’s GTVs.
184
Figure 4-4 A profile of the pixel values taken through the identified GTV on a PET AC image The graph of the profile through the VOI is giving the units for the PET AC image as CT numbers. This is unit is displayed for all images (CT, MRI, PET or SPECT) but the tool is providing the actual image pixel.
4.2.2.2 RO contouring trial using patient data
The patient image data was prepared for the ROs to contour in the trials. Two plans
were created for each patient, one with the CT scan only and the other with pre-
registered CT and PET AC scans. The CT and PET scans were registered using the
translations and rotations from the baseline registrations results for each patient
image data sets in the registration trials. Separate copies of the two plans created for
each patient were made for each RO so as to avoid any comparison or biasing of
their individual results if they all contoured on the same plan.
185
The participating ROs were provided with a number of resources to use including:
• The patient’s clinical notes and PET report to assist them with locating
the malignant disease on the images.
• Reference information on PET image reconstruction and interpretation.
• Instructions from the Pinnacle3
• A set of instructions (see Appendix 6), detailing the methodology to be
used for the GTV delineation trials was supplied to the ROs.
user manual for the Syntegra program.
The ROs were instructed to perform the following steps for each patient (see
Appendix 6 for the specific use of the Syntegra software for the contouring):
1. On the plan with the CT image only
• Adjust viewing window widths and levels depending on the location of
the primary (Lung or Thorax preset)
• Outline the primary separately from any involved nodes and label each
discrete volume of disease as GTV1, GTV2 etc.
2. On the plan with the registered CT/PET data
• Adjust the window widths and levels as required:
o CT (Lung or Thorax preset).
o PET (either to view background and intense uptake or intense uptake
regions only).
• Locate the middle of each region of intense uptake corresponding to
malignant disease on the PET AC scan.
• Determine the maximum pixel value of each VOI using a line profile
through its mid-region.
• Use the semi automated contouring technique with the threshold values
(ranging from 20% - 30%), to contour the primary and any involved
nodes (labelling each contour as previously instructed for the CT only
GTVs).
• The ROs were allowed to edit the results of the semi-automated
contouring technique.
3. The ROs were requested to not compare their registration results with the other
ROs participating in the trial.
186
4.2.3 Data analysis
The mean and standard deviation of the RO contours on the CT image only and the
registered CT and PET AC images were calculated.
RO contouring trial using patient data
The percentage difference in the combined RO contours for each patient was
calculated for both the contours performed using the CT image only and those
performed using the registered CT and PET AC images and the contouring protocol
provided. The percentage difference of the combined contours was determined by:
1. Adding each of the RO contours together for each patient for the CT only
contoured GTVs.
2. Subtracting each individuals RO’s contours from the combined contour to obtain
a contour that represented the difference of each RO’s contour from the
combined contour.
3. Each of the contours representing the difference of each RO’s contour from the
combined contour were combined to create a contour which represented the
voxels of the CT image which were not mutually contoured by all the ROs.
4. The volume of the contour which represented the voxels of the CT image which
were not mutually contoured by all the ROs divided by the volume of the contour
which combined each of the RO’s contours, resulting in the percentage difference
of the combined RO contours.
5. Steps 1 -5 were repeated for the RO contours performed using the registered CT
and PET AC images and the contouring protocol.
187
4.2.4 Results
4.2.4.1 Pilot study and RO training
The results from the pilot study used to determine the threshold values for GTV
definition on the patient PET AC images are shown in Table 4-4. It was found that a
threshold value range of 20% to 30% was appropriate for contouring the identified
primary tumour and involved lymph nodes for patients 2 – 9. A 20% threshold value
was required where the primary tumour was mostly or completely surrounded by
lung tissue. A threshold value range of 25% - 30% was required where the primary
tumour and the involved lymph nodes were surrounded mostly by non-lung tissue
(i.e. the mediastinum or the chest wall). These threshold values were visually
correlated on the patient CT images using the PET viewing window protocols
developed in Chapter 2.
Table 4-4 Results of the pilot study used to determine the threshold values for contouring the GTV on the patient PET AC images
Patient Tissue
surrounding the GTV VOI
Background-activity ratios to be used for contouring max VOI
diameter (cm)
Selected contouring threshold
value Liver or
lung-based Background-activity ratio
1 non-lung liver-based 76.6 nodes = 1.0 excluded from study
2 non-lung and lung liver and lung-based
21.8 (liver) 9.3 (lung) primary = 2.5 25
3 predominately non-lung liver-based 27.5 primary = 6.5 30
4 non-lung and lung liver and lung-based
23.5 (liver) 10.3 (lung)
nodes = 1.5 primary = 5.5
30 (nodes) 25 (primary)
5 predominately non-lung tissue liver-based 19.4 primary = 8.5 30
6 predominately lung tissue lung-based 5.1 primary = 6.5 20
7 non-lung and lung liver and lung-based
27.5 (liver) 10.7 (lung)
nodes = 1.5 primary = 4.5
30 (nodes) 20 (primary)
8 lung tissue lung-based 9.7 primary = 2.5 20
9 predominately non-lung tissue liver-based 4.8 primary = 10.5 30
It was decided to exclude patient 1 from the RO contouring trial during the pilot
study. This patient had undergone a right pneumonectomy 2 years prior to their
188
current presentation for treatment with lymph node recurrence in the right upper
chest wall and mediastinum. A combination of altered anatomical structure position
and the high background-activity ratio relative to the involved lymph nodes would
add to the complexity of defining contours using this patient’s PET AC image.
It was found that use of the profile tool to determine the maximum pixel value in the
VOI to be contoured, was a suitable alternative to using the MBS tools. The profile
tool would not be as precise as determining the maximum using the MBS model of a
sphere converted to a contour encompassing the VOI on the PET AC image. For
example the profile results shown in Figure 4-4 are for patient 3. The maximum
pixel value would be estimated at 8000, with a 20% threshold value equalling 1600.
The maximum pixel in the VOI to be contoured is 8022 based on the data extracted
from the contour generated from the MBS model of a sphere. The 20% threshold
value based on this data would be 1604.
4.2.4.2 RO contouring trial using patient data
One of the 6 ROs withdrew from the trial due to work commitments, so only 5 ROs
completed the contouring trial. Images of the contouring results for the 5 different
ROs are shown in Figures 4-5 to 4-7. The contours using the CT only and the
registered CT and PET AC images for each RO are overlaid on each image.
189
Figure 4-5 RO contouring results: Patients 2 – 4
Patient 2 CT only Patient 2 CT/PET
Patient 3 CT only Patient 3 CT/PET
Patient 4 CT only Patient 4 CT/PET
190
Figure 4-6 RO contouring results: Patients 5 – 7
Patient 5 CT only Patient 4 CT/PET
Patient 6 CT only Patient 6 CT/PET
Patient 7 CT only Patient 7 CT/PET
191
Figure 4-7 RO contouring results: Patients 8 and 9
Patient 8 CT only Patient 8 CT/PET
Patient 9 CT only Patient 9 CT/PET
192
The mean and standard deviation of the RO contours on the CT image only and the
registered CT and PET AC images for each patient are shown in Table 4-5. The
percentage difference values (see Table 4-6) indicate the percentage of the total
voxels included in the combined RO contours which were not commonly contoured
by all the ROs.
Table 4-5 The mean volumes of the RO contours
Contours Patient
2 3 4 5 6 7 8 9
CT image only
Mean volume (cm3 113.8 ) 125.6 100.7 144.3 208.4 27.5 2.3 494.2
Standard deviation (%) 48 23 93 47.9 16.2 21.5 21.8 10.5
CT/PET images
Mean volume (cm3 20.3 ) 124.0 83.0 255.3 203.6 45.9 5.0 426.6
Standard deviation (%) 17 31 28 12 15.6 33 42 15
Table 4-6 The percentage differences in the volumes of the RO contours
Contours Patient
2 3 4 5 6 7 8 9
CT image only
Combined volume (cm3 209.5 ) 191.7 275.4 314.9 298.0 46.7 3.6 656.4
Combined difference (cm3 182.4 ) 108.3 239.4 250.7 137.6 31.8 1.7 258.8
% combined difference 87.1 56.5 86.9 79.6 46.2 68.2 48.0 39.4
CT/PET images
Combined volume (cm3 28.6 ) 176.4 147.1 314.3 248.2 90.2 8.3 534.2
Combined difference (cm3 10.0 ) 89.9 82.3 74.2 69.7 61.6 4.6 138.1
% combined difference 35.0 51.0 56.0 23.6 28.1 68.3 55.9 25.8
Images of the percentage differences between 5 different RO’s contours are shown in
Figures 4-8 to 4-10. The blue contours demonstrate the voxels not commonly
contoured by all 5 ROs when the contours were defined using the CT image only.
The yellow contours demonstrate the voxels not commonly contoured by all of the
ROs on the registered CT and PET AC images.
193
Figure 4-8 The percentage differences for the combined RO contours: Patients 2 – 4
Patient 2 CT only Patient 2 CT/PET
Patient 3 CT only Patient 3 CT/PET
Patient 4 CT only Patient 4 CT/PET
194
Figure 4-9 The percentage differences for the combined RO contours: Patients 5 – 7
Patient 5 CT only Patient 5 CT/PET
Patient 6 CT only Patient 6 CT/PET
Patient 7 CT only Patient 7 CT/PET
195
Figure 4-10 The percentage differences for the combined RO contours: Patients 8 and 9
Patient 8 CT only Patient 8 CT/PET
Patient 9 CT only Patient 9 CT/PET
196
4.2.5 Discussion and conclusions
The pilot study was necessary as the effects of particular patient specific conditions
on the selection of contour threshold values could not be tested on the phantom
images. The uptake in the central rods and the sphere were homogenous while
heterogeneous uptake can occur within tumours (this effect was clearly demonstrated
in patient 3’s PET image). The moving sphere was surrounded by air on the
phantom and that none of the tumours in the patient images were surrounded by
tissues with 0% background activities (an issue also highlighted when the viewing
window protocols determined on the phantom images were applied to the patient
images).
It was found that a threshold values ranging from 20 – 30% were suitable for
contouring the 8 patient images which were to be included in the RO contouring
trials. The tumour dimensions and background activity ratios determined on each
patient’s PET image (see Table 4-4) were used to interpolate the phantom contouring
results to factor in the effects of motion. These threshold values were visually
correlated on the patient CT images using the PET viewing window protocols
developed in Chapter 2.
A 20% threshold value was suitable for contouring tumour volumes not fixed to the
chest wall or mediastinum (i.e. more heavily influenced by respiration with larger
ranges of motion67
), which was higher than the 10% threshold value which correctly
contoured the moving sphere in air on the phantom. This increase in threshold value
for moving volumes surrounded by tissues with background activity does follow the
pattern observed in phantom tests; that contouring threshold values will increase as
background activity increases. The 30% threshold was suitable for the smaller nodes
and primaries affected by respiration and the larger tumours which were less heavily
influenced by respiration. This range of threshold values (based on the maximum
pixel value measured within a VOI) did not under-estimate the dimensions of
tumours with heterogeneous uptake (see Figure 4-5). The maximum pixel value was
selected for the clinical protocol trial as the SUV data was not available for each
patient’s PET AC image.
197
The clinical trial of the contouring protocol found that consistent primary tumour
volumes across different oncologists could be obtained using the registered CT/PET
images, compared to those contoured using the planning CT scans only. However
the trial also demonstrated that variations in the contouring of regional lymph nodes
were not eliminated using the CT/PET fusion contouring protocol. The largest
reduction in the percentage differences for the combined RO contours for the PET
based contours were seen for patients 2 and 5, where the boundaries of the tumour
were not clearly defined on the CT images. Even when the patients had well defined
primary volumes on the CT images, the percentage differences were less for the
CT/PET contours than the CT only contours. Patient 8 is the exception to this with
the percentage difference of the combined CT-only contours = 48.0% while the
CT/PET contours = 55.9%. The increase in the percentage difference for this patient
may be a product of the PET image resolution and the small dimensions of the
tumour. For the patients with mediastinal lymph node involvement there was large
variation in the delineation of these nodes. This is most likely due to differences in
opinion between the ROs as to which lymph nodes to include particularly when
uptake in the nodes was not clearly indicative of malignancy.
The ROs were allowed to edit their contours in the trial for two reasons. The semi-
automated technique can contour individual voxels outside of the GTV with
intensities similar to the threshold intensity depending on where the cursor is placed
as the start point for the automatic generation of the contour. Close proximity of the
tumour to normal tissues which have higher levels of uptake can result in the normal
tissues being included in the GTV volume. Similar problems are encountered when
thresholds and the automatic contouring tools are used to contour normal tissues and
structures on planning CT scans.
The semi-automated adaptive threshold contouring technique and the viewing
window protocols to the patient images was successfully applied by the participating
ROs in the GTV definition trial. Feedback from the ROs indicated that initially they
found the technique a bit laborious especially using the profile tool to determine the
VOI maximum (instead of using the MBS tools to load a spherical model over the
primary and involved nodes). Some of the ROs commented that once they became
more familiar with the technique they found that it cut down on their contouring time
198
because they were not manually contouring the GTVs slice by slice on the registered
CT and PET images.
While the RO contouring trial can only be considered a proof of concept study due to
the small number of patients, it clearly demonstrated that the use of CT/PET fusion
contouring protocol can reduce inter-user variation in GTV definition. Riegal et al106
demonstrated that contouring on registered CT/PET images without windowing
protocols did not reduce differences in contoured GTVs. Berson et al129 performed a
follow-up study which found that viewing and contouring protocols did show
improvement in the consistency of GTVs contoured by multiple radiologists and
radiation oncologists. The ROs were provided with a tutorial prior to their
participation in the GTV contouring trials for this research project. Berson et al’s129
study also provided a tutorial in their trial prior to participants contouring the patient
images, highlighting the importance of training when any protocols are implemented
to further reduce inter-user variation.
199
5 The outcomes of the quality assurance phantom study were used to develop clinical
protocols which minimised potential errors in the CT/PET fusion process to improve
the accuracy of GTV localisation for 3DCRT lung cancer patients. Image
acquisition, PET viewing, image registration and CT-PET fusion protocols were
developed and tested.
Conclusions and recommendations
• The image acquisition protocol successfully identified candidates for CT/PET
fusion in advance of their staging PET scan to facilitate positioning of the
patient in their radiotherapy treatment position.
• The phantom study determined that PET viewing windows which provided
an accurate display of volumes was dependant on tumour-background ratios.
A protocol based on window widths and levels relative to the maximum
image pixel value was more easily applied to different patient specific
conditions.
• The automated registration can provide sub-voxel levels of accuracy when
the CT and PET images are registered. Fiducial markers were not found to be
of benefit in the image registration process. Image registration protocols
which factored in potential software-based errors combined with adequate
user training are recommended to increase the accuracy and reproducibility of
registration outcomes.
• A semi-automated adaptive threshold contouring technique accurately defines
the geometric edge of a tumour volume using PET image data from a stand
alone PET scanner, including 4D target volumes. The contouring protocols,
incorporating the PET viewing protocols demonstrated that consistent
primary tumour volume results across different oncologists could be
obtained. However it also demonstrated that variations in the contouring of
regional lymph nodes were not eliminated in some of the patient data studied.
It is recommended that this study is adapted to different treatment planning systems
and for PET image data obtained from combined CT-PET scanners.
200
Plots of the maximum and mean pixel values obtained from the TPS contouring tools for each PET AC image: Series 1-4
Appendix 1: Phantom PET scan pixel and SUV graphs
Plots (a) – (d) are of the maximum pixel value in each of the 3D contours of the different components of the phantom for each AC scan in the indicated PET scan series Plots (e) – (h) are of the mean pixel value in each of the 3D contours of the different components of the phantom on each AC scan in the indicated PET scan series
Phantom PET scan Series 1
0.0
2000.0
4000.0
6000.0
8000.0
10000.0
12000.0
14000.0
16000.0
a b c d e fScan
Max
pix
el n
umbe
r
Phantom PET scan Series 1
0.01000.02000.03000.04000.05000.06000.07000.08000.09000.0
a b c d e f
Scan
Mea
n pi
xel n
umbe
r
(a) (e)
Phantom PET scan Series 2
0.0
10000.0
20000.0
30000.0
40000.0
50000.0
60000.0
70000.0
a b c d e fScan
Max
pix
el n
umbe
r
Phantom PET scan Series 2
0.0
5000.0
10000.0
15000.0
20000.0
25000.0
a b c d e fScan
Mea
n pi
xel n
umbe
r
(b) (f)
Phantom PET scan Series 3
0.0
2000.0
4000.0
6000.0
8000.0
10000.0
12000.0
14000.0
a b c d e fScan
Max
pix
el n
umbe
r
Phantom PET scan Series 3
0.0
1000.0
2000.0
3000.0
4000.0
5000.0
6000.0
7000.0
a b c d e fScan
Mea
n pi
xel n
umbe
r
(c) (g)
Phantom PET scan Series 4
0.0
5000.0
10000.0
15000.0
20000.0
25000.0
30000.0
35000.0
a b c d e fScan
Max
pix
el n
umbe
r
Phantom PET scan Series 4
0.02000.04000.06000.08000.0
10000.012000.014000.016000.018000.0
a b c d e fScan
Mea
n pi
xel n
umbe
r
(d) (h)
201
Plots of the maximum and mean pixel values obtained from the TPS contouring tools for each PET AC image: Series 5-8 Plots (a) – (d) are of the maximum pixel value in each of the 3D contours of the different components of the phantom for each AC scan in the indicated PET scan series Plots (e) – (h) are of the mean pixel value in each of the 3D contours of the different components of the phantom on each AC scan in the indicated PET scan series
Phantom PET scan Series 5
0.01000.02000.03000.04000.05000.06000.07000.08000.09000.0
a b c d e fScan
Max
pix
el n
umbe
r
Phantom PET scan Series 5
0.0
1000.0
2000.0
3000.0
4000.0
5000.0
6000.0
a b c d e fScan
Mea
n pi
xel n
umbe
r
(a) (e)
Phantom PET scan Series 6
0.0
1000.0
2000.0
3000.0
4000.0
5000.0
6000.0
7000.0
8000.0
9000.0
a b c d e fScan
Max
pix
el n
umbe
r
Phantom PET scan Series 6
0.0
1000.0
2000.0
3000.0
4000.0
5000.0
6000.0
a b c d e fScan
Mea
n pi
xel n
umbe
r
(b) (f)
Phantom PET scan Series 7
0.0
1000.0
2000.0
3000.0
4000.0
5000.0
6000.0
7000.0
8000.0
9000.0
a b c d e fScan
Max
pix
el n
umbe
r
Phantom PET scan Series 7
0.0
1000.0
2000.0
3000.0
4000.0
5000.0
6000.0
a b c d e fScan
Mea
n pi
xel n
umbe
r
(c) (g)
Phantom PET scan Series 8
0.01000.02000.03000.04000.05000.06000.07000.08000.09000.0
10000.0
a b c d e fScan
Max
pix
el n
umbe
r
Phantom PET scan Series 8
0.0
1000.0
2000.0
3000.0
4000.0
5000.0
6000.0
a b c d e fScan
Mea
n pi
xel n
umbe
r
(d) (h)
202
Plots of the maximum and mean SUV values obtained from the TPS contouring tools for each PET AC image: Series 1-4 Plots (a) – (d) are of the maximum SUV value in each of the 3D contours of the different components of the phantom for each AC scan in the indicated PET scan series Plots (e) – (h) are of the mean SUV value in each of the 3D contours of the different components of the phantom on each AC scan in the indicated PET scan series
Phantom PET scan Series 1
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
a b c d e fScan
Max
SU
V va
lue
Phantom PET scan Series 1
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
a b c d e fScan
Mea
n SU
V va
lue
(a) (e)
Phantom PET scan Series 2
0.0
50.0
100.0
150.0
200.0
250.0
300.0
350.0
a b c d e fScan
Max
SU
V va
lue
Phantom PET scan Series 2
0.0
20.0
40.0
60.0
80.0
100.0
120.0
a b c d e fScan
Mae
anSU
V va
lue
(b) (f)
Phantom PET scan Series 3
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
a b c d e fScan
Max
SU
V va
lue
Phantom PET scan Series 3
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
a b c d e fScan
Mea
n SU
V va
lue
(c) (g)
Phantom PET scan Series 4
0.020.040.060.080.0
100.0120.0140.0160.0180.0200.0
a b c d e fScan
Max
SU
V va
lue
Phantom PET scan Series 4
0.0
20.0
40.0
60.0
80.0
100.0
120.0
a b c d e fScan
Mea
n SU
V va
lue
(d) (h)
203
Plots of the maximum and mean SUV values obtained from the TPS contouring tools for each PET AC image: Series 5-8 Plots (a) – (d) are of the maximum SUV value in each of the 3D contours of the different components of the phantom for each AC scan in the indicated PET scan series Plots (e) – (h) are of the mean SUV value in each of the 3D contours of the different components of the phantom on each AC scan in the indicated PET scan series
Phantom PET scan Series 5
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
a b c d e fScan
Max
SU
V va
lue
Phantom PET scan Series 5
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
a b c d e fScan
Mea
n SU
V va
lue
(a) (e)
Phantom PET scan Series 6
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
a b c d e fScan
Max
SU
V va
lue
Phantom PET scan Series 6
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
a b c d e fScan
Mea
n SU
V va
lue
(b) (f)
Phantom PET scan Series 7
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
a b c d e fScan
Max
SU
V va
lue
Phantom PET scan Series 7
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
a b c d e fScan
Mea
n SU
V va
lue
(c) (g)
Phantom PET scan Series 8
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
a b c d e fScan
Max
SU
V va
lue
Phantom PET scan Series 8
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
a b c d e fScan
Mea
n SU
V va
lue
(d) (h)
204
Post registration results of registering the same PET image (series 1 scan (a)) of the phantom with itself using the CC algorithm with varying pre-registration translation and rotation offsets
Appendix 2: Graphs of the registration algorithm test results
Translation only pre-registration offsets
0
2
4
6
8
10
12
14
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) 1cm offset-1cm offset2.0cm offset-2.0 offset3.0cm offset-3.0cm offset4.0cm offset -4.0cm offset5.0cm offset-5.0cm offset
5cm translation pre-registration offset
0
2
4
6
8
10
12
14
16
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) 0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-5cm translation pre-registration offset
0
2
4
6
8
10
12
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) -0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
205
Post registration results of registering the two different PET images of the same phantom conditions (series 1-scan (a) and series 1-scan (b)) using the CC algorithm with varying pre-registration translation and rotation offsets
Translation only pre-registration offsets
0
2
4
6
8
10
12
14
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
1cm offset-1cm offset2.0cm offset-2.0 offset3.0cm offset-3.0cm offset4.0cm offset -4.0cm offset5.0cm offset-5.0cm offset
1cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) 0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-1cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rotcm
(tra
nsla
tions
) deg
rees
(rot
atio
ns)
-0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
2cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) 0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-2cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) -0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
3cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) 0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-3cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) -0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
4cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) 0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-4cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) -0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
5cm translation pre-registration offset
0
5
10
15
20
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) 0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-5cm translation pre-registration offset
0
2
4
6
8
10
12
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) -0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
206
Post registration results of registering the same CT image (static CT scan1) with itself using the MI algorithm with varying pre-registration translation and rotation offsets
Translation only pre-registration offsets
0.00
0.20
0.40
0.60
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
1cm offset-1cm offset2.0cm offset-2.0 offset3.0cm offset-3.0cm offset4.0cm offset -4.0cm offset5.0cm offset-5.0cm offset
1cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
0.5degree rotation
1.0degree rotation
1.5degree rotation
2.0degree rotation
-1cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
-0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
2cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-2cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
-0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
3cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-3cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
-0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
4cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-4cm translation pre-registration offset
0.0
2.0
4.0
6.0
8.0
10.0
x trans y trans z trans x rot y rot z rotcm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
-0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
5cm translation pre-registration offset
0.0
5.0
10.0
15.0
20.0
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation3.0degree rotation4.0degree rotation5.0degree rotation
-5cm translation pre-registration offset
0
20
40
60
80
100
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
-0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation-3.0degree rotation-4.0degree rotation-5.0degree rotation
207
Post registration results of registering the same PET image (series 1 scan (a)) of the phantom with itself using the MI algorithm with varying pre-registration translation and rotation offsets
Translation only pre-registration offsets
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
1cm offset
-1cm offset
2.0cm offset
-2.0 offset
3.0cm offset
-3.0cm offset
4.0cm offset
-4.0cm offset
5.0cm offset
-5.0cm offset
1cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-1cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rotcm
(tra
nsla
tions
) deg
rees
(rot
atio
ns)
-0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
2cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) 0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-2cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
-0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
3cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) 0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-3cm translation pre-registration offset
0.0
0.2
0.4
0.6
0.8
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) -0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
4cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-4cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) -0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
5cm translation pre-registration offset
0.0
0.2
0.4
0.6
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation3.0degree rotation4.0degree rotation5.0degree rotation
-5cm translation pre-registration offset
0
5
10
15
20
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
-0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation-3.0degree rotation-4.0degree rotation-5.0degree rotation
208
Post registration results of registering the two different PET images of the same phantom conditions (series 1-scan (a) and series 1-scan (b)) using the MI algorithm with varying pre-registration translation and rotation offsets
Translation only pre-registration offsets
0
5
10
15
20
25
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
)
1cm offset-1cm offset2.0cm offset-2.0 offset3.0cm offset-3.0cm offset4.0cm offset -4.0cm offset5.0cm offset-5.0cm offset
1cm translation pre-registration offset
0.0
0.4
0.8
1.2
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) 0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-1cm translation pre-registration offset
0.0
0.4
0.8
1.2
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) -0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
2cm translation pre-registration offset
0.0
0.4
0.8
1.2
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) 0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-2cm translation pre-registration offset
0.0
0.4
0.8
1.2
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) -0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
3cm translation pre-registration offset
0
4
8
12
16
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) 0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-3cm translation pre-registration offset
0
4
8
12
16
20
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) -0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
4cm translation pre-registration offset
0.0
0.4
0.8
1.2
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) 0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation
-4cm translation pre-registration offset
0
4
8
12
16
20
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) -0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation
5cm translation pre-registration offset
0
5
10
15
20
25
30
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) 0.5degree rotation1.0degree rotation1.5degree rotation2.0degree rotation3.0degree rotation4.0degree rotation5.0degree rotation
-5cm translation pre-registration offset
0
5
10
15
20
25
30
x trans y trans z trans x rot y rot z rot
cm (t
rans
latio
ns) d
egre
es (r
otat
ions
) -0.5degree rotation-1.0degree rotation-1.5degree rotation-2.0degree rotation-3.0degree rotation-4.0degree rotation-5.0degree rotation
209
Box plots of post-registration results (difference from baseline registrations) for each registration translation and rotation parameter. Registration was performed using either the PET AC scan or the PET transmission scan.
Appendix 3: Graphs of the phantom registration results
Registrations using PET scan series 1-3 and 5 images (n=6 registrations)
210
Registrations using PET scan series 4 images (n=6 registrations)
211
Registrations using PET scan series 6 images (n=6 registrations)
212
Registrations using PET scan series 7 images (n=6 registrations)
213
Registrations using PET scan series 8 images (n=6 registrations)
214
RT instructions Appendix 4: RT instructions for the patient image registration trials
General instructions
1. You will be given an identifying name by which all your patient data sets will be labelled
2. There are 9 patient CT and PET data sets. 3. For each patient you are required to register the images
• Manually using the CT and PET emission scan • Automatically using the:
− CT and PET emission scans or − CT, PET transmission and PET emission scans
4. Two plans have been created for each patient. The image data has already been imported for each plan. The two plans have the following names:
• Manual – for the manual registrations • Auto – for the registrations performed using the Syntegra auto-fusion
algorithms 5. You have been provided with some reference information on PET image
reconstruction and interpretation as well as some of the instructions from the Pinnacle user manual for the Syntegra program.
6. You are asked not to compare your outcomes with another participant in the trial.
Viewing and windowing the data sets
1. For the CT data: • Select “Raw” for the Units option. This will result in all CT data
being given as CT numbers • Use the window width and level presets that you would normally use
(Abdomen or breast work well for all of the CT data sets in the trial). 2. For the PET transmission scans:
• Use either the “greyscale” or “thermal” for the 2D colour option • Select “% Max” for the Units option. This will result in all PET data
being represented as a percentage of the value of the scan • Set the window to 0.4 and the level to 0.2 (NB This is a suggested
starting point, you can adjust these settings) 3. For the PET emission scans:
• Use either the “greyscale” or “thermal” for the 2D colour option • Select “% Max” for the Units option. This will result in all PET data
being represented as a percentage of the max value of the scan • Suggested starting window widths and levels for the PET data is as
follows (you can adjust these settings if you wish): − The window to 0.2 and the level to 0 as a starting point when
matching air/tissue surfaces between the CT and PET data − The window to 0.4 and the level to 0 as a starting point when
matching internal features between the CT and PET data
215
Manual registration
1. All of the 9 patients need to be manually registered. You are required to manually translate and rotate the PET emission scan to register it with the planning CT scan.
2. Select the plan named Manual that has been created for each patient and enter the Syntegra platform.
3. In the Set-up window choose the appropriate 2D colour and window width and level as outlined previously. You can adjust the window width and levels suggested to achieve better visualisation of the data if required.
4. You will need to change your window widths and levels of the PET emission scan depending on whether you are trying to match air/tissues surfaces or internal features.
5. Initially you may need to zoom the coronal image down to locate the position of the PET emission scan.
6. Use only the manual 2D and rotation and translation tools. 7. Once you have registered the CT and PET data exit and save the data.
Automatic registration
There are two ways that you will be required to register the data using the Syntegra
auto-fusion algorithms. The method you use to automatically register the patient CT
and PET data depends on whether the patient data includes only a PET emission
scan or includes both a PET transmission and emission scan. The patient plan
information will be labelled accordingly to identify the available data so that you
know which method to use to register the patient.
1. Auto-registering patient data with a CT and PET emission scan • There are 4 patients with only the CT and PET emission scans.
1. Select the plan named “Auto” for the relevant patient. 2. In the Set-up window choose the appropriate 2D colour and window width
and level as outlined previously. You can adjust the suggested window width and levels to achieve better visualisation of the data if required.
3. Click the “Move secondary centre to primary centre” button. This will move the absolute middle of the secondary scan to the absolute middle of the CT data.
4. If the lungs surfaces on the PET data is sitting above the CT data then manually move the PET data so that the lungs on the PET data are roughly aligned with those on the CT data.
5. In the Fusion window select the appropriate registration parameters • Select CT-PET emission • then Normalised Mutual Information • then Proceed with fusion
6. Do not use the Limit image sets functions. 7. Save and exit the plan.
216
2. Auto-registering patient data with a CT and PET transmission and emission scans
• There are 5 patients with CT and PET transmission and emission scans.
1. Select the plan named “Auto” for the relevant patient. 2. You have two secondary images – one is the transmission scan the other is
the emission scan. The transmission scan should be the one with the (2) in front of the scan name (eg. (2)XXXXX, XXXXX). You can select the image you want to work with by clicking on the button next to secondary image in the set-up window and selecting appropriate image set.
3. In the Set-up window choose the appropriate 2D colour and window width and level as outlined previously for all the data sets (CT, PET transmission and emission scans). You can adjust the suggested window width and levels to achieve better visualisation of the data if required.
4. Select the transmission scan ((2)XXXXX, XXXXX) as the secondary data set.
5. Make sure that both data sets available for registration that are shown in the Available fusion image sets are set to Moveable.
6. Click the Move secondary centre to primary centre button. This will move the absolute middle of the secondary scan to the absolute middle of the CT data.
8. If the lungs surfaces on the PET data is sitting above the CT data then manually move the PET data so that the lungs on the PET data are roughly aligned with those on the CT data.
9. In the Fusion window select the appropriate registration parameters • Select CT-PET transmission • then Normalised Mutual Information • then Proceed with fusion
10. In the bottom right hand corner of the Fusion window under Copy this transformation to, select the PET emission scan then press Go.
11. Do not use the Limit image sets functions. 12. Save and exit the plan.
217
Box plots of post-registration results (difference from baseline registrations) for each registration translation and rotation parameter. Registration was performed either manually or automatically using the MI algorithm.
Appendix 5: Graphs of the results for the RT registration trials
Registrations results for patient 1 - 3 (n=12 registrations)
218
Registrations results for patient 4 - 6 (n=12 registrations)
219
Registrations results for patient 7 - 9 (n=12 registrations)
220
Appendix 6: RO instructions for the patient GTV definition trials
221
222
223
224
225
226
227
228
229
230
6 1. Maintz JB, Viergever MA. A survey of medical image registration. Med Image
Anal 1998;2(1):1-36.
Bibliography
2. Thornton AF, Jr., Sandler HM, Ten Haken RK, McShan DL, Fraass BA, La Vigne ML, et al. The clinical utility of magnetic resonance imaging in 3-dimensional treatment planning of brain neoplasms. Int J Radiat Oncol Biol Phys 1992;24(4):767-75.
3. Kooy HM, van Herk M, Barnes PD, Alexander E, 3rd, Dunbar SF, Tarbell NJ, et al. Image fusion for stereotactic radiotherapy and radiosurgery treatment planning. Int J Radiat Oncol Biol Phys 1994;28(5):1229-34.
4. Gambhir SS, Czernin J, Schwimmer J, Silverman DHS, Coleman RE, Phelps ME. A tabulated summary of the FDG PET literature. J Nucl Med 2001;42 Suppl(5):1S-93S.
5. Paulino AC, Johnstone PAS. FDG-PET in radiotherapy treatment planning: Pandora's box? Int J Radiat Oncol Biol Phys 2004;59(1):4-5.
6. Keyes JW, Jr. SUV: standard uptake or silly useless value? J Nucl Med 1995;36(10):1836-9.
7. Chen GT, Kung JH, Beaudette KP. Artifacts in computed tomography scanning of moving objects. Semin Radiat Oncol 2004;14(1):19-26.
8. Bakheet SM, Saleem M, Powe J, Al Amro A, Larsson SG, Mahassin Z. F-18 fluorodeoxyglucose chest uptake in lung inflammation and infection. Clin Nucl Med 2000;25(4):273-278.
9. Withers HR. Biological basis of radiation therapy for cancer. Lancet 1992;339(8786):156-9.
10. International Commission on Radiation Units and Measurements (ICRU) Report 50. Prescribing, recording, and reporting photon beam therapy. Bethesda, MD, USA; 1993.
11. International Commission on Radiation Units and Measurements (ICRU) Report 62. Prescribing, recording, and reporting photon beam therapy (supplement to ICRU Report 50). Bethesda, MD, USA; 1999.
12. Purdy JA. Current ICRU definitions of volumes: limitations and future directions. Semin Radiat Oncol 2004;14(1):27-40.
13. Purdy JA. Dose to normal tissues outside the radiation therapy patient's treated volume: a review of different radiation therapy techniques. Health Phys 2008;95(5):666-76.
14. Emami B, Lyman J, Brown A, Coia L, Goitein M, Munzenrider JE, et al. Tolerance of normal tissue to therapeutic irradiation. Int J Radiat Oncol Biol Phys 1991;21(1):109-22.
231
15. Tsujino K, Hirota S, Endo M, Obayashi K, Kotani Y, Satouchi M, et al. Predictive value of dose-volume histogram parameters for predicting radiation pneumonitis after concurrent chemoradiation for lung cancer. Int J Radiat Oncol Biol Phys 2003;55(1):110-5.
16. Fraass BA. The development of conformal radiation therapy. Med Phys 1995;22(11 Pt 2):1911-21.
17. Aird EG, Conway J. CT simulation for radiotherapy treatment planning. Br J Radiol 2002;75(900):937-49.
18. Tyldesley S, Boyd C, Schulze K, Walker H, Mackillop WJ. Estimating the need for radiotherapy for lung cancer: an evidence-based, epidemiologic approach. Int J Radiat Oncol Biol Phys 2001;49(4):973-85.
19. The Royal College of Radiologists Clinical Oncology Information Network. Guidelines on the non-surgical management of lung cancer. Clin Oncol (R Coll Radiol) 1999;11(1):S1-S53.
20. Sause WT. The role of radiotherapy in non-small cell lung cancer. Chest 1999;116(6 Suppl):504S-508S.
21. Marks LB, Sibley G. The rationale and use of three-dimensional radiation treatment planning for lung cancer. Chest 1999;116(6 Suppl):539S-545S.
22. Coleman RE. PET in lung cancer. J Nucl Med 1999;40(5):814-820.
23. Van de Steene J, Linthout N, de Mey J, Vinh-Hung V, Claassens C, Noppen M, et al. Definition of gross tumor volume in lung cancer: inter-observer variability. Radiother Oncol 2002;62(1):37-49.
24. Giraud P, Elles S, Helfre S, De Rycke Y, Servois V, Carette M-F, et al. Conformal radiotherapy for lung cancer: different delineation of the gross tumor volume (GTV) by radiologists and radiation oncologists. Radiother Oncol 2002;62(1):27-36.
25. Senan S, van Sornsen de Koste J, Samson M, Tankink H, Jansen P, Nowak PJ, et al. Evaluation of a target contouring protocol for 3D conformal radiotherapy in non-small cell lung cancer. Radiother Oncol 1999;53(3):247-55.
26. Fritscher-Ravens A, Bohuslavizki KH, Brandt L, Bobrowski C, Lund C, Knofel T, et al. Mediastinal lymph node involvement in potentially resectable lung cancer - Comparison of CT, positron emission tomography, and endoscopic ultrasonography with and without fine-needle aspiration. Chest 2003;123(2):442-451.
27. MacManus MP, Hicks RJ, Ball DL, Kalff V, Matthews JP, Salminen E, et al. F-18 fluorodeoxyglucose positron emission tomography staging in radical radiotherapy candidates with nonsmall cell lung carcinoma. Cancer 2001;92(4):886-895.
232
28. Photon Treatment Planning Collaborative Working Group. Three-dimensional display in planning radiation therapy: a clinical perspective. Int J Radiat Oncol Biol Phys 1991;21(1):79-89.
29. Sherouse GW, Novins K, Chaney EL. Computation of digitally reconstructed radiographs for use in radiotherapy treatment design. Int J Radiat Oncol Biol Phys 1990;18(3):651-8.
30. Galvin JM, Sims C, Dominiak G, Cooper JS. The use of digitally reconstructed radiographs for three-dimensional treatment planning and CT-simulation. Int J Radiat Oncol Biol Phys 1995;31(4):935-42.
31. Thomas SJ. Relative electron density calibration of CT scanners for radiotherapy treatment planning. Br J Radiol 1999;72(860):781-6.
32. Ramm U, Damrau M, Mose S, Manegold KH, Rahl CG, Bottcher HD. Influence of CT contrast agents on dose calculations in a 3D treatment planning system. Phys Med Biol 2001;46(10):2631-5.
33. Rosenman JG, Miller EP, Tracton G, Cullip TJ. Image registration: an essential part of radiation therapy treatment planning. Int J Radiat Oncol Biol Phys 1998;40(1):197-205.
34. Bradley JD, Perez CA, Dehdashti F, Siegel BA. Implementing biologic target volumes in radiation treatment planning for non-small cell lung cancer. J Nucl Med 2004;45 Suppl 1:96S-101S.
35. Baker GR. Localization: conventional and CT simulation. Br J Radiol 2006;79(Special Issue 1):S36-49.
36. Erdi YE, Rosenzweig K, Erdi AK, Macapinlac HA, Hu YC, Braban LE, et al. Radiotherapy treatment planning for patients with non-small cell lung cancer using positron emission tomography (PET). Radiother Oncol 2002;62(1):51-60.
37. Mah K, Caldwell CB, Ung YC, Danjoux CE, Balogh JM, Ganguli SN, et al. The impact of (18)FDG-PET on target and critical organs in CT-based treatment planning of patients with poorly defined non-small-cell lung carcinoma: a prospective study. Int J Radiat Oncol Biol Phys 2002;52(2):339-50.
38. van Herk M. Errors and margins in radiotherapy. Semin Radiat Oncol 2004;14(1):52-64.
39. van Herk M, Remeijer P, Rasch C, Lebesque JV. The probability of correct target dosage: dose-population histograms for deriving treatment margins in radiotherapy. Int J Radiat Oncol Biol Phys 2000;47(4):1121-35.
40. McKenzie A CM, Greemer T, Hall C, Van Herk M, Mijnheer B, Harrison A. Chapter2: Technical overview of geometrical uncertainties in radiotherapy. In: Geometric uncertainties in radiotherapy: The British Institute of Radiology; 2003. p. 11-27.
233
41. Armstrong JG. Target volume definition for three-dimensional conformal radiation therapy of lung cancer. Br J Radiol 1998;71(846):587-94.
42. Fraass B, Doppke K, Hunt M, Kutcher G, Starkschall G, Stern R, et al. American Association of Physicists in Medicine Radiation Therapy Committee Task Group 53: quality assurance for clinical radiotherapy treatment planning. Med Phys 1998;25(10):1773-829.
43. Hutton BF, Braun M, Thurfjell L, Lau DY. Image registration: an essential tool for nuclear medicine. Eur J Nucl Med Mol Imaging 2002;29(4):559-77.
44. Garcia-Ramirez JL, Mutic S, Dempsey JF, Low DA, Purdy JA. Performance evaluation of an 85-cm-bore X-ray computed tomography scanner designed for radiation oncology and comparison with current diagnostic CT scanners. Int J Radiat Oncol Biol Phys 2002;52(4):1123-31.
45. Mutic S, Palta JR, Butker EK, Das IJ, Huq MS, Loo LN, et al. Quality assurance for computed-tomography simulators and the computed-tomography-simulation process: report of the AAPM Radiation Therapy Committee Task Group No. 66. Med Phys 2003;30(10):2762-92.
46. Mulkens TH, Bellinck P, Baeyaert M, Ghysen D, Van Dijck X, Mussen E, et al. Use of an automatic exposure control mechanism for dose optimization in multi-detector row CT examinations: clinical evaluation. Radiology 2005;237(1):213-23.
47. Greess H, Nomayr A, Wolf H, Baum U, Lell M, Bowing B, et al. Dose reduction in CT examination of children by an attenuation-based on-line modulation of tube current (CARE Dose). Eur Radiol 2002;12(6):1571-6.
48. Flohr TG, Schaller S, Stierstorfer K, Bruder H, Ohnesorge BM, Schoepf UJ. Multi-detector row CT systems and image-reconstruction techniques. Radiology 2005;235(3):756-73.
49. Wilting JE, Timmer J. Artefacts in spiral-CT images and their relation to pitch and subject morphology. Eur Radiol 1999;9(2):316-22.
50. Centre for Evidence Based Purchasing. Report 05071 Siemens Somatom Sensation Open CT scanner technical evaluation: NHS Purshasing and Supply Agency; 2005.
51. Heiken JP, Brink JA, Vannier MW. Spiral (helical) CT. Radiology 1993;189(3):647-56.
52. Balter JM, Lam KL. Technical note: acquisition of CT models for radiotherapy applications with reduced tube heating. Med Phys 2001;28(4):590-2.
53. Fahey FH. Data Acquisition in PET Imaging. J Nucl Med Technol 2002;30(2):39-49.
234
54. Huang SC, Hoffman EJ, Phelps ME, Kuhl DE. Quantitation in positron emission computed tomography: 3 Effect of sampling. J Comput Assist Tomogr 1980;4(6):819-26.
55. Meikle S, Dahlbon M. Positron Emission Tomography. In: Murray IPC, Ell PJ, Van der Wall H, editors. Nuclear medicine in clinical diagnosis and treatment. 2nd ed. Edinburgh ; London: Churchill Livingstone; 1998. p. 1603-1638.
56. Hoffman EJ, Huang SC, Phelps ME. Quantitation in positron emission computed tomography: 1. Effect of object size. J Comput Assist Tomogr 1979;3(3):299-308.
57. Kessler RM, Ellis JR, Jr., Eden M. Analysis of emission tomographic scan data: limitations imposed by resolution and background. J Comput Assist Tomogr 1984;8(3):514-22.
58. Mazziotta JC, Phelps ME, Plummer D, Kuhl DE. Quantitation in positron emission computed tomography: 5. Physical--anatomical effects. J Comput Assist Tomogr 1981;5(5):734-43.
59. Brambilla M, Matheoud R, Secco C, Sacchetti G, Comi S, Rudoni M, et al. Impact of target-to-background ratio, target size, emission scan duration, and activity on physical figures of merit for a 3D LSO-based whole body PET/CT scanner. Med Phys 2007;34(10):3854-65.
60. Surti S, Karp JS. Imaging characteristics of a 3-dimensional GSO whole-body PET camera. J Nucl Med 2004;45(6):1040-9.
61. Lonneux M, Borbath I, Bol A, Coppens A, Sibomana M, Bausart R, et al. Attenuation correction in whole-body FDG oncological studies: the role of statistical reconstruction. Eur J Nucl Med 1999;26(6):591-8.
62. Raylman RR, Kison PV, Wahl RL, Kress J, Minohara S, Endo M, et al. Capabilities of two- and three-dimensional FDG-PET for detecting small lesions and lymph nodes in the upper torso: a dynamic phantom study. Patient position verification using CT images. Eur J Nucl Med 1999;26(1):39-45.
63. Strauss LG, Conti PS. The applications of PET in clinical oncology. J Nucl Med 1991;32(4):623-48.
64. Kim CK, Gupta NC, Chandramouli B, Alavi A. Standardized uptake values of FDG: body surface area correction is preferable to body weight correction. J Nucl Med 1994;35(1):164-7.
65. Graham MM, Peterson LM, Hayward RM. Comparison of simplified quantitative analyses of FDG uptake. Nucl Med Biol 2000;27(7):647-55.
66. American Association of Physicists in Medicine Task Group 76: Report 91. The management of respiratory motion in radiation oncology. College Park, MD: American Association of Physicists in Medicine; 2006.
235
67. Seppenwoolde Y, Shirato H, Kitamura K, Shimizu S, van Herk M, Lebesque JV, et al. Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. Int J Radiat Oncol Biol Phys 2002;53(4):822-34.
68. Shirato H, Seppenwoolde Y, Kitamura K, Onimura R, Shimizu S. Intrafractional tumor motion: lung and liver. Semin Radiat Oncol 2004;14(1):10-8.
69. Keall P. 4-dimensional computed tomography imaging and treatment planning. Semin Radiat Oncol 2004;14(1):81-90.
70. Caldwell CB, Mah K, Skinner M, Danjoux CE. Can PET provide the 3D extent of tumor motion for individualized internal target volumes? A phantom study of the limitations of CT and the promise of PET. Int J Radiat Oncol Biol Phys 2003;55(5):1381-93.
71. Bradley J, Thorstad WL, Mutic S, Miller TR, Dehdashti F, Siegel BA, et al. Impact of FDG-PET on radiation therapy volume delineation in non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 2004;59(1):78-86.
72. Australian Government Department of Health and Ageing. Medicare Benefits Schedule Book 1 November. 2004; http://www.health.gov.au/internet/mbsonline/publishing.nsf/Content/MBSOnline-2004.
73. Michael G. X-ray computed tomography. Phys Educ 2001(6):442.
74. Von Schulthess GK Kacl G Stumpe DM. The normal PET scan. In: Von Schulthess GK, editor. Clinical positron emission tomography (PET): correlation with morphological cross-sectional imaging. Philadelphia Pa; London: Lippincott Williams & Wilkins; 1999. p. 49-69.
75. Ak I, Stokkel MP, Pauwels EK. Positron emission tomography with 2-[18F]fluoro-2-deoxy-D-glucose in oncology. Part II. The clinical value in detecting and staging primary tumours. J Cancer Res Clin Oncol 2000;126(10):560-74.
76. Delbeke D. Oncological applications of FDG PET imaging: brain tumors, colorectal cancer, lymphoma and melanoma. J Nucl Med 1999;40(4):591-603.
77. Hawkes DJ. Registration methodology: Introduction. In: Hajnal JV, Hawkes DJ, Hill D, editors. Medical image registration. Boca Raton: CRC Press; 2001. p. 11-38.
78. Slomka P. Software approach to merging molecular with anatomic information. Journal of Nuclear Medicine 2004;45(1):36S.
79. Brown LG. A Survey of Image Registration Techniques. ACM Computing Surveys 1992;24(4):325-376.
236
80. Cai J, Chu JC, Recine D, Sharma M, Nguyen C, Rodebaugh R, et al. CT and PET lung image registration and fusion in radiotherapy treatment planning using the chamfer-matching method. Int J Radiat Oncol Biol Phys 1999;43(4):883-91.
81. Hill D, Batchelor P. Chapter 3: Registration methodology: Concepts and Algorithms. In: Hajnal JV, Hawkes DJ, Hill D, editors. Medical image registration. Boca Raton: CRC Press; 2001. p. 11-38.
82. Mutic S, Dempsey JF, Bosch WR, Low DA, Drzymala RE, Chao KS, et al. Multimodality image registration quality assurance for conformal three-dimensional treatment planning. Int J Radiat Oncol Biol Phys 2001;51(1):255-60.
83. Owen R, Kron T, Foroudi F, Cox J, Zhu L, Cramb J, et al. The detectability and localization accuracy of implanted fiducial markers determined on in-room computerized tomography (CT) and electronic portal images (EPI). Med Dosim 2008;33(3):226-33.
84. van Herk M, Kooy HM. Automatic three-dimensional correlation of CT-CT, CT-MRI, and CT-SPECT using chamfer matching. Med Phys 1994;21(7):1163-78.
85. Studholme C, Hill DL, Hawkes DJ. Automated 3-D registration of MR and CT images of the head. Med Image Anal 1996;1(2):163-75.
86. Studholme C, Hill DLG, Hawkes DJ. Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures. Med Phys 1997;24(1):25-35.
87. Rösch P, Netsch T, McNutt T, Shoenbill J, Root P. Syntegra: Automated image registration algorithms. Syntegra™ White Paper: Philips Medical Systems; 2003.
88. Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P. Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 1997;16(2):187-98.
89. Maes F, Vandermeulen D, Suetens P. Medical image registration using mutual information. Proceedings of the IEEE 2003;91(10):1699-1722.
90. Slomka PJ, Dey D, Przetak C, Aladl UE, Baum RP. Automated 3-dimensional registration of stand-alone (18)F-FDG whole-body PET with CT. J Nucl Med 2003;44(7):1156-67.
91. Walimbe V, Shekhar R. Automatic elastic image registration by interpolation of 3D rotations and translations from discrete rigid-body transformations. Med Image Anal 2006;10(6):899-914.
237
92. Goerres GW, Kamel E, Heidelberg TN, Schwitter MR, Burger C, von Schulthess GK. PET-CT image co-registration in the thorax: influence of respiration. Eur J Nucl Med Mol Imaging 2002;29(3):351-60.
93. Slomka P. Software approach to merging molecular with anatomic information. J Nucl Med 2004;45(1):36S.
94. Zhang Y, Chu JC, Hsi W, Khan AJ, Mehta PS, Bernard DB, et al. Evaluation of four volume-based image registration algorithms. Med Dosim 2009;34(4):317-22.
95. Daisne J-F, Sibomana M, Bol A, Cosnard G, Lonneux M, Grégoire V. Evaluation of a multimodality image (CT, MRI and PET) coregistration procedure on phantom and head and neck cancer patients: accuracy, reproducibility and consistency. Radiother Oncol 2003;69(3):237-245.
96. Dey D, Slomka PJ, Hahn LJ, Kloiber R. Automatic three-dimensional multimodality registration using radionuclide transmission CT attenuation maps: A phantom study. J Nucl Med 1999;40(3):448.
97. Skalski J, Wahl RL, Meyer CR. Comparison of mutual information-based warping accuracy for fusing body CT and PET by 2 methods: CT mapped onto PET emission scan versus CT mapped onto PET transmission scan. J Nucl Med 2002;43(9):1184.
98. Lavely WC, Scarfone C, Cevikalp H, Li R, Byrne DW, Cmelak AJ, et al. Phantom validation of coregistration of PET and CT for image-guided radiotherapy. Med Phys 2004;31(5):1083-92.
99. Wong JC, Studholme C, Hawkes DJ, Maisey MN. Evaluation of the limits of visual detection of image misregistration in a brain fluorine-18 fluorodeoxyglucose PET-MRI study. Eur J Nucl Med 1997;24(6):642-50.
100. Pekar V, McNutt TR, Kaus MR. Automated model-based organ delineation for radiotherapy planning in prostatic region. Int J Radiat Oncol Biol Phys 2004;60(3):973-80.
101. Munley MT, Marks LB, Scarfone C, Sibley GS, Patz EF, Jr., Turkington TG, et al. Multimodality nuclear medicine imaging in three-dimensional radiation treatment planning for lung cancer: challenges and prospects. Lung Cancer 1999;23(2):105-14.
102. Black QC, Grills IS, Kestin LL, Wong CY, Wong JW, Martinez AA, et al. Defining a radiotherapy target with positron emission tomography. Int J Radiat Oncol Biol Phys 2004;60(4):1272-82.
103. Grills IS, Yan D, Black QC, Wong CY, Martinez AA, Kestin LL. Clinical implications of defining the gross tumor volume with combination of CT and 18FDG-positron emission tomography in non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 2007;67(3):709-19.
238
104. Patz EF, Lowe VJ, Hoffman JM, Paine SS, Burrowes P, Coleman RE, et al. Focal pulmonary abnormalities: evaluation with F-18 fluorodeoxyglucose PET scanning. Radiology 1993;188(2):487-490.
105. Erdi YE, Mawlawi O, Larson SM, Imbriaco M, Yeung H, Finn R, et al. Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding. Cancer 1997;80(12 Suppl):2505-9.
106. Riegel AC, Berson AM, Destian S, Ng T, Tena LB, Mitnick RJ, et al. Variability of gross tumor volume delineation in head-and-neck cancer using CT and PET/CT fusion. Int J Radiat Oncol Biol Phys 2006;65(3):726-32.
107. Steenbakkers RJ, Duppen JC, Fitton I, Deurloo KE, Zijp LJ, Comans EF, et al. Reduction of observer variation using matched CT-PET for lung cancer delineation: a three-dimensional analysis. Int J Radiat Oncol Biol Phys 2006;64(2):435-48.
108. National Health and Medical Research Council Australian Research Council Australian Vice-Chancellors’ Committee. National Statement on Ethical Conduct in Human Research. 2007; http://www.nhmrc.gov.au/publications/synopses/_files/e72-jul09.pdf.
109. Boellaard R, Krak NC, Hoekstra OS, Lammertsma AA. Effects of noise, image resolution, and ROI definition on the accuracy of standard uptake values: a simulation study. J Nucl Med 2004;45(9):1519-27.
110. Brown TF, Yasillo NJ. Radiation safety considerations for PET centers. J Nucl Med Technol 1997;25(2):98-102.
111. Soret M, Riddell C, Hapdey S, Buvat I. Biases affecting the measurements of tumor-to-background activity ratio in PET. IEEE Trans Nucl Sci 2002;49(5):2112-2118.
112. Nestle U, Kremp S, Schaefer-Schuler A, Sebastian-Welsch C, Hellwig D, Rube C, et al. Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-Small cell lung cancer. J Nucl Med 2005;46(8):1342-8.
113. Schinagl DA, Vogel WV, Hoffmann AL, van Dalen JA, Oyen WJ, Kaanders JH. Comparison of five segmentation tools for 18F-fluoro-deoxy-glucose-positron emission tomography-based target volume definition in head and neck cancer. Int J Radiat Oncol Biol Phys 2007;69(4):1282-9.
114. Hong R, Halama J, Bova D, Sethi A, Emami B. Correlation of PET standard uptake value and CT window-level thresholds for target delineation in CT-based radiation treatment planning. Int J Radiat Oncol Biol Phys 2007;67(3):720-726.
115. Flohr TG, Schaller S, Stierstorfer K, Bruder H, Ohnesorge BM, Schoepf UJ. Multi-Detector Row CT Systems and Image-Reconstruction Techniques. Radiology 2005;235(3):756-773.
239
116. Pan T, Mawlawi O. PET/CT in radiation oncology. Med Phys 2008;35(11):4955-66.
117. Paquet N, Albert A, Foidart J, Hustinx R. Within-patient variability of (18)F-FDG: standardized uptake values in normal tissues. J Nucl Med 2004;45(5):784-8.
118. Wang Y, Chiu E, Rosenberg J, Gambhir SS. Standardized uptake value atlas: characterization of physiological 2-deoxy-2-[18F]fluoro-D-glucose uptake in normal tissues. Mol Imaging Biol 2007;9(2):83-90.
119. Bland JM, Altman DG. Agreement between methods of measurement with multiple observations per individual. J Biopharm Stat 2007;17(4):571-82.
120. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999;8(2):135-60.
121. Rousson V, Gasser T, Seifert B. Assessing intrarater, interrater and test-retest reliability of continuous measurements. Stat Med 2002;21(22):3431-46.
122. Yaremko B, Riauka T, Robinson D, Murray B, Alexander A, McEwan A, et al. Thresholding in PET images of static and moving targets. Phys Med Biol 2005;50(24):5969-82.
123. Okubo M, Nishimura Y, Nakamatsu K, Okumura M, Shibata T, Kanamori S, et al. Static and moving phantom studies for radiation treatment planning in a positron emission tomography and computed tomography (PET/CT) system. Ann Nucl Med 2008;22(7):579-86.
124. Erdi YE, Nehmeh SA, Pan T, Pevsner A, Rosenzweig KE, Mageras G, et al. The CT motion quantitation of lung lesions and its impact on PET-measured SUVs. J Nucl Med 2004;45(8):1287-92.
125. Pan T, Mawlawi O, Nehmeh SA, Erdi YE, al e. Attenuation Correction of PET Images with Respiration-Averaged CT Images in PET/CT. J Nucl Med 2005;46(9):1481.
126. Nehmeh SA, Erdi YE, Pan T, Pevsner A, Rosenzweig KE, Yorke E, et al. Four-dimensional (4D) PET/CT imaging of the thorax. Med Phys 2004;31(12):3179-86.
127. MacManus M, Nestle U, Rosenzweig KE, Carrio I, Messa C, Belohlavek O, et al. Use of PET and PET/CT for radiation therapy planning: IAEA expert report 2006-2007. Radiother Oncol 2009;91(1):85-94.
128. Nestle U, Kremp S, Grosu AL. Practical integration of [18F]-FDG-PET and PET-CT in the planning of radiotherapy for non-small cell lung cancer (NSCLC): the technical basis, ICRU-target volumes, problems, perspectives. Radiother Oncol 2006;81(2):209-25.
129. Berson AM, Stein NF, Riegel AC, Destian S, Ng T, Tena LB, et al. Variability of gross tumor volume delineation in head-and-neck cancer using PET/CT
240
fusion, Part II: the impact of a contouring protocol. Med Dosim 2009;34(1):30-5.
top related