patient dose verification for image-guided radiation...
TRANSCRIPT
Patient dose verification for image-guided radiation therapy using
a deformable registration tool
Amanda Jean Sprague Dyess
Master of Science
Medical Physics Unit
McGill University
Montreal, Quebec
December, 2012
A thesis submitted to the Faculty of Graduate Studies and Research of
McGill University in partial fulfillment of the requirements of the degree of
Master of Science
© Amanda Jean Sprague Dyess, 2012
All rights reserved. This dissertation may not be reproduced in whole or in
part by photocopy or other means, without the permission of the author.
ii
Dedication
To my husband Ron for his endless support and encouragement.
iii
Acknowledgements
First I would like to thank my supervisors, William Parker and Dr. Emily Poon for their
guidance and insight throughout this project.
I would like to acknowledge Dr. George Shenouda for his time in modifying head and
neck patient contours, and Dr. Rolina Al-Wassia for her help with the craniospinal
irradiation patients.
I thank the Medical Physics Unit staff at the Montreal General Hospital for their
willingness to answer my many questions and offer their assistance. I would like to thank
Dr. Maritza Hobson for her help with cone beam CT, Dr. Emilie Soisson for her help
with TomoTherapy troubleshooting, and Dr. Marija Popovic for all the clinical advice. I
must also thank Margery Knewstubb and Tatjana Nisic for all their administrative help.
I acknowledge my fellow medical physics students for their discussions, help and lively
lunchtime conversations, specifically Ian Gerard for translating my abstract into French.
I would like to thank MIM Software for providing the hardware for this project and their
helpful customer support.
Lastly, I should thank my friend, Kelly Hall who convinced me to go into the field of
medical physics.
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Abstract
Patient geometry often changes during the course of radiation therapy due to factors such
as weight loss, tumor and normal tissue growth or shrinkage, and intra-treatment position
variations. It has been shown that these changes can cause the dose delivered to differ
from the originally planned dose distribution. Often this will result in the need to create a
modified treatment plan, a process known as adaptive radiation therapy. The aim of this
thesis is to evaluate the dosimetric effects due to anatomical changes and positioning
variations during intensity-modulated radiation therapy through two retrospective studies.
MIM Software (Cleveland, OH), a commercially available deformable registration tool,
is used for this work.
In the first study, the daily dose for four breast cancer patients undergoing a volumetric
modulated arc boost treatment to the tumor bed is calculated on pretreatment cone beam
computed tomography images. Two treatment isocenters, corresponding to the initial
patient set up position, and the shifted position based on pretreatment imaging, are used
for dose verification. The results indicate that a planning target volume consisting of the
tumor bed and a uniform margin expansion of 1 cm is adequate to account for positioning
errors.
In the second study, the daily dose is calculated on the pretreatment megavoltage
computed tomography images for craniospinal irradiation and head and neck cancer
patients undergoing helical tomotherapy. The dose for each treatment fraction is
deformed and accumulated to the planning computed tomography image for comparison
with the original plan. This study assesses the effects of anatomical changes on treatment
delivery. The results indicate a slight decrease in target coverage and no significant
increase in dose to critical structures.
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Résumé
Souvent, au cours d'une procédure de radiothérapie, la géométrie du patient change en
raison de facteurs tels que la perte de poids, la croissance ou le rétrécissement des tissus
tumoral et normal, et les variations de position en cours de traitement. Il a été démontré
que ces changements peuvent faire en sorte que la dose de radiation administrée soit
différente de la dose initialement planifiée. Souvent, cela se traduit par la nécessité de
créer un plan de traitement alternatif, un processus appelé radiothérapie adaptative.
L'objectif de cette thèse est d'évaluer les effets dosimétriques causés par des changements
anatomiques ainsi que les variations de positionnement au cours de la procédure de
radiothérapie avec modulation d'intensité à travers deux études rétrospectives. MIM
Software (Cleveland, OH), un outil d'enregistrement non-linéaire disponible sur le
marché, est utilisé pour ce travail.
Dans la première étude, la dose quotidienne de quatre patients atteints de cancer du sein
qui subissent un traitement volumétrique modulée arc-boost dans le lit tumoral est
calculée à partir des images prétraitement de tomodensitométrie à faisceau conique. Deux
isocentres de traitement, correspondant à la position initiale du patient et à la position
ajustée à partir d'imagerie prétraitement, sont utilisés pour la vérification de la dose. Les
résultats indiquent que le volume cible prévisionnel comprenant le lit tumoral et une
augmentation de la marge uniforme de 1 cm sont suffisants pour tenir compte des erreurs
de positionnement.
Dans la deuxième étude, la dose quotidienne est calculée sur les images prétraitement de
tomodensitométrie à mégavoltage pour l'irradiation craniospinale et aux patients atteints
de cancer de la tête et du cou qui obtiennent la tomothérapie hélicoïdale. La dose pour
chaque fraction de traitement est déformée et accumulés sur le CT de planification pour
être comparée avec le plan original. Cette étude évalue les effets des changements
anatomiques sur l'administration du traitement. Les résultats indiquent une légère
diminution de la couverture cible et aucune augmentation significative de la dose pour les
structures critiques.
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TABLE OF CONTENTS
Dedication…………………………………………………………... ii
Acknowledgements…………………………………………………. iii
Abstract…………………………………………………………….. iv
Resume……………………………………………………………... v
List of tables………………………………………………………... ix
List of figures………………………………………………………. x
1 Introduction………………………………………………………. 1
1.1 Cancer……………………………………………………….. 1
1.2 Radiation therapy…………………………………………… 2
1.3 Prescription and volume definition for treatment planning…. 5
1.3.1 Assessment of treatment delivered…………………… 6
1.4 Anatomical changes during treatment………………………. 7
1.4.1 Effect on dose distribution…………………………… 8
1.5 Image guided radiation therapy…………………………….. 9
1.6 Adaptive radiation therapy………………………………….. 10
1.7 Image registration…………………………………………… 12
1.7.1 Dose accumulation…………………………………… 13
1.8 Purpose of thesis and outline………………………………... 13
References………………………………………………………….. 14
2 Theory………………………………………………………….… 16
2.1 Image registration algorithms……………………………..... 16
2.1.1 Rigid registration………………………………………. 17
2.1.2 Deformable registration……………………………….. 17
2.1.3 MIM Software…………………………………………. 19
2.1.4 Dose deformation……………………………………… 19
2.2 Similarity measures………………………………………….. 20
2.3 Treatment planning process…………………………………. 21
2.3.1 Forward and inverse planning…………………………. 21
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2.3.2 Dose calculation algorithms…………………………… 22
2.3.3 Tissue inhomogeneity corrections…………………….. 22
References………………………………………………………….. 24
3 Materials and methods…………………………………………… 25
3.1 MIM deformation validation………………………………… 26
3.2 Hounsfield Unit to density calibration…...…………………. 27
3.3 Breast boost dose verification study………………………… 28
3.3.1 Patients………………………………………………… 28
3.3.2 Daily imaging…………………………………………. 29
3.3.3 Merged image…………………………………………. 30
3.3.4 Daily dose calculations………………………………... 31
3.3.5 Dose deformation and accumulation………………….. 31
3.4 Planned Adaptive treatment verification study……………… 32
3.4.1 Craniospinal irradiation patients………………………. 32
3.4.2 Head and neck patient…………………………………. 33
3.4.3 Helical TomoTherapy treatment delivery…………….. 33
3.4.4 Planned Adaptive module……………………………... 33
References………………………………………………………….. 35
4 Results and discussion…………………….……………………… 36
4.1 Validation of MIM Software………………………………… 36
4.1.1 Contour deformation…………………………………... 36
4.1.2 Dose deformation……………………………………… 38
4.2 Breast boost study…………………………………………… 39
4.3 Planned Adaptive treatment verification study……………… 44
4.3.1 Craniospinal irradiation study…………………………. 44
4.3.2 Head and neck study...………………………………… 48
4.4 Limitations………………………………………….……….. 51
References………………………………………………………….. 53
5 Conclusion………………………………….……………………. 55
5.1 Dose verification studies……………………………………. 55
5.2 Future work…………………………………………………. 56
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Abbreviations……………………………………………………… 57
References…………………………………………………………. 58
ix
List of tables
Table Page
3.1 – Breast patients prescription and field characteristics for the four VMAT
plans…………………………………………………………………………. 29
3.2 – Daily shifts applied to patient prior to treatment based on CBCT image
verification…………………………………………………………………… 30
3.3 – CSI patients diagnosis and prescription……………………………………… 33
4.1 – Contour deformation validation values………………………………………. 37
4.2 – Dose volume statistics for four breast patients as calculated in the shifted
position……………………………………………………………………….. 40
4.3 - Dose volume statistics for four breast patients as calculated in the un-shifted
pre-treatment position………………………………………………………… 41
4.4 – Ratio of MVCT calculated dose to original plan dose for the five CSI
patients………………………………………………………………………... 47
4.5 – Absolute dose difference in D50% (MVCT-original) for the heart and kidneys
for the CSI patients…………………….……………………………………... 48
x
List of Figures
Figure Page
1.1 - Dose distribution for and head and neck cancer patient comparing IMRT and
3D-CRT………………………………………………………………………. 4
1.2 – The Varian’s Trilogy linac and the TomoTherapy’s Hi-Art system…………. 5
1.3 – Cumulative DVH for a target and organ at risk……………………………… 7
1.4 – CT images of a head and neck cancer patient at the start of treatment and 5
weeks into treatment…………………………………………………………. 7
1.5 – Image registration between MVCT and planning CT………………………... 12
2.1 – HU calibration curves for kV CT, MVCT, and CBCT images……………… 23
3.1 – Tissue characterization phantom used for the derivation of the Hounsfield
Unit-to-density conversion curve…………………………………………….. 28
3.2 – Workflow for breast boost dose verification study…………………………... 28
3.3 – Merged image of CBCT and CT for a breast cancer patient created in MIM... 30
3.4 – VMAT beams on CBCT image and the corresponding dose colorwash for a
prescription of 2.5 Gy per fraction…………………………………………… 31
3.5 – Workflow for Planned Adaptive treatment verification study………………. 32
3.6 – Planned and verification dose isodose lines shown on a merged image for a
CSI patient…………………………………………………………………… 34
4.1 – Ratio of near maximum dose to spinal cord as calculated on the MVCT in
Planned Adaptive to the deformed dose for each fraction………………….. 38
4.2 - Near maximum dose to the spinal cord and spinal cord PRV for each
fraction as calculated on the MVCT in Planned Adaptive and also deformed
to the original CT in MIM……………………………………………………. 39
4.3 - DVH for patient 2 showing dose the the PTV (top) an GTV (bottom)
for each fraction (blue) compared with the original plan (red) and the
accumulated shifted plan (pink)………………………………………………. 42
4.4 – Accumulated D50% for the heart, left lung, and right lung as calculated in the
shifted and un-shifted position compared with the original plan dose..……… 43
xi
4.5 – Volume of the CTV receiving the prescription dose for each fraction of CSI
treatment…………………………………………………………………….. 44
4.6 – Ratio of dose-volume indices calculated on the pretreatment MVCT images
to that of the original plan…………………………………………………… 45
4.7 – The median dose to the CTV volume for each fraction of the CSI treatment
as calculated on each MVCT…………………………………………………. 46
4.8 – Ratios of MVCT accumulated to original plan dose-volume indices for the
heart, kidneys, and lungs for the five CSI patients…………………………... 47
4.9 – Volume receiving 95% of the prescription dose for all PTVs calculated for
each fraction………………………………………………………………….. 49
4.10 – Median dose to the three PTVs for each fraction…………………………… 49
4.11 – Mean dose to the parotids for each fraction calculated using the Planned
Adaptive software……………………………………………………………. 50
4.12 – Near maximum dose per fraction for the spinal cord and spinal cord PRV… 51
1
Chapter One
Introduction
Contents
1.1 Cancer………………………………………………………. 1
1.2 Radiation therapy…………………….................................... 2
1.3 Prescription and volume definition for treatment planning…. 5
1.3.1 Assessment of treatment delivered…………………..... 6
1.4 Anatomical changes during treatment………………………. 7
1.4.1 Effect on dose distribution……….................................. 8
1.5 Image guided radiation therapy…………………................... 9
1.6 Adaptive radiation therapy……………………….................. 10
1.7 Image registration…………………….................................... 12
1.7.1 Dose accumulation……………………………………. 13
1.8 Purpose of thesis and outline………………………………... 13
1.1 Cancer
Cancer is a disease in which abnormal cells in the body begin to grow out of control. As
they continue to grow and produce more abnormal cells, they can begin to invade other
tissues. These cancer cells can also spread to other parts of the body, forming new tumors
called metastases [1]. It is estimated that in 2012, there will be 186,400 new cases of
cancer (excluding 81,300 non-melanoma skin cancers) and 75,700 deaths from cancer in
Canada. Four types of cancer (breast, lung, colorectal, and prostate) will make up 53% of
the new cases in 2012, with breast and prostate being the most common types of cancer
in women and men respectively [2].
Patients diagnosed with cancer are generally treated with surgery, chemotherapy, or
radiation therapy; often a combination of at least two approaches is used. Surgery is used
to physically remove the tumor as well as to biopsy the surrounding tissue to check for
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microscopic disease. Chemotherapy is the use of drugs to treat cancer, while radiation
therapy is the use of ionizing radiation to kill cancerous cells.
1.2 Radiation therapy
Radiation therapy can be in the form of external beam radiation therapy (EBRT), most
commonly delivered with photon, electron, or proton beams, or in the form of
brachytherapy which uses sealed radioactive sources to treat tumors either internally or
superficially [3].
As a result of advances in imaging as well as radiation delivery, EBRT has developed
over time to allow for more conformal dose distributions. Photon EBRT has developed
from the simple techniques used in conventional radiation therapy to modern techniques
including three-dimensional conformal radiation therapy (3D-CRT) and intensity
modulated radiation therapy (IMRT). These techniques are often delivered as fractionated
daily treatments over the course of a few weeks, up to two months. Stereotactic
radiosurgery and stereotactic body radiation therapy are specialized techniques that aim
to destroy a small tumor volume with a large radiation dose over one or a few fractions.
Historically, conventional radiation therapy treatments were delivered using linear
accelerators (linacs) or cobalt units, with mounted lead or cerrobend shielding blocks to
shape the beam. Typically, parallel opposed or four-field box beam geometries were
used. Treatment planning was done using kilovoltage (kV) projection radiographs taken
with a simulator that had a similar table and gantry arrangement as an isocentric linac.
This allows the simulation setup to mimic that of the actual treatment. The planar images
were not able to localize the tumor precisely, and hence the treatment fields required
significant margins around the target to ensure dose coverage. Megavoltage (MV) portal
images were used for comparison with simulation radiographs to ensure correct patient
setup and for treatment verification.
With the increasing use of computed tomography (CT) for treatment simulation in the
1980s, patient anatomy could now be visualized in three dimensions (3D). This allows
for better delineation of targets and organs at risk (OAR). Treatment beam geometry and
shielding can thus be made to conform better to the target volume, reducing radiation
3
damage to the nearby healthy tissue. This technique is referred to as 3D-CRT [4].
Physical and virtual wedges as well as multileaf collimators (MLC) gradually replaced
shielding blocks for shaping fields. Digitally reconstructed radiographs, which are
projection images derived from CT images, are compared with pre-treatment imaging for
setup verification.
In the latter part of the 1990s, IMRT began to be used clinically [5]. IMRT uses intensity-
modulated beams from multiple directions to deliver a highly conformal dose
distribution. MLC is often used to modulate the shape and intensity of the treatment field.
This is achieved either by a series of static MLC field segments (“step-and-shoot”) or by
using a dynamic MLC where the leaf pairs move across the field while the beam is on.
With IMRT, it is possible to treat multiple targets to different dose levels concurrently.
This technique is known as simultaneous integrated boost. Dynamic IMRT often requires
an inverse treatment planning approach, which makes use of an optimization algorithm to
minimize cost functions related to the treatment objectives [4]. An optimal plan is
generated based upon the dose volume constraints for the targets, OAR, and other
planning structures as specified by the user. A comparison of the IMRT and 3D-CRT
treatment plans for a head and neck cancer patient is shown in Figure 1.1.
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Figure 1.1. Dose distributions for a head and neck cancer patient comparing IMRT with
3D-CRT. The top shows the IMRT distribution and the bottom the 3D-CRT distribution
for the same patient. The 100% isodose level in the bottom panel corresponds to the
prescription dose of 50 Gy in the top. (Image from [6])
Newer forms of IMRT deliveries have emerged from the conventional multiple-field
technique in the past decade. These include volumetric modulated arc therapy (VMAT)
and helical tomotherapy (HT). In VMAT, MLC leaves are in motion as the linac gantry
rotates around the patient while the beam is on. In addition to the varying MLC aperture
shape, dose rate and gantry rotation speed are also varied throughout the arc delivery.
Treatment plan optimization is done using an aperture-based algorithm to maximize
target dose conformity and minimize beam-on time [7]. One or more arcs that rotate up to
360° around the patient are used, depending on the complexity of the plan.
Literally meaning “slice therapy”, HT uses a rotating fan-beam delivery [8] in which the
patient is translated through the gantry of a rotating radiation source [9]. The
TomoTherapy Hi-Art System (Accuray, Sunnyvale, CA) uses a 6 MV photon beam that
maintains a set nominal dose rate during treatment. In treatment planning, each full
gantry rotation is divided into 51 sections for which the 64 leaves of the binary MLC are
assigned by the inverse optimizer with unique leaf-opening times. The user can control
the field width, beam blocking, as well as the speed of couch travel relative to gantry
rotation. The system also provides megavoltage CT (MVCT) imaging capability,
5
allowing the therapists to register the pre-treatment MVCT with the planning CT for
proper patient setup. A picture of a linac and a HT unit are shown in Figure 1.2.
Figure 1.2. The Varian’s Trilogy linac (left) and the TomoTherapy’s Hi-Art System
(right).
1.3 Prescription and volume definitions for treatment planning
The International Commission on Radiation Units and Measurements (ICRU) has
published several reports whose recommendations for radiation therapy prescription and
target volume definitions are widely accepted. For photon beam radiation therapy, these
reports are ICRU 50 and 62 [10,11]. For IMRT, there is ICRU 83 [4]. The ICRU 50
defines three primary volumes used for treatment planning. The gross tumor volume
(GTV) is defined as: “the gross palpable or visible/demonstrable extent and location of
malignant growth.” The clinical target volume (CTV) is defined as: “a tissue volume that
contains a demonstrable GTV and/or subclinical microscopic malignant disease, which
has to be eliminated.” The planning target volume (PTV) is defined as: “a geometrical
concept, and it is defined to select appropriate beam sizes and beam arrangements, taking
into consideration the net effect of all the possible geometrical variations, in order to
ensure that the prescribed dose is actually absorbed in the CTV” [10].
For describing normal tissues the ICRU defines several other volumes. The irradiated
volume is the volume of tissue which receives a significant dose in relation to normal
tissue tolerances. An organ at risk (OAR) is a normal structure whose radiation sensitivity
might influence treatment planning or prescribed dose. Analogous to the PTV is the
6
concept of the planning organ at risk volume (PRV), which is a margin created around an
OAR to account for position variations during treatment.
Also addressed in the ICRU reports is dose prescribing and reporting based on absorbed
dose and volume information. The maximum and minimum dose (Dmax and Dmin)
correspond to the highest and lowest dose received by any volume of a structure. A more
clinically significant concept is that of the near-maximum and near-minimum doses. The
near-maximum dose is defined as the dose received by 2% of the volume (D2%), and the
near-minimum dose is the dose received by 98% of the volume (D98%). The mean and
median doses are often used to report dose to OAR. The median dose (D50%) is the dose
received by 50% of the volume. The mean dose (Dmean) is the integral dose divided by the
volume. It is defined in ICRU 83 as:
∫
( )
(1.1)
where dV(D)/dD is the increment of volume per absorbed dose at absorbed dose, D, and
V is the volume of the structure [4].
1.3.1 Assessment of treatment delivered
At our institution, the majority of IMRT plans are prescribed such that 95% of the target
volume receives 100% of the prescription dose. ICRU report 50 recommends that the
target volume receive as homogeneous a dose as possible by limiting the dose gradient
within the volume to +7% and -5% of the prescription dose [10]. When evaluating plans,
one is concerned with both target coverage and sparing of the critical structures. Plans
can be evaluated using isodose distributions as well as cumulative dose volume
histograms (DVHs). DVHs are a graphical way of viewing volumetric information about
the dose received by a given structure, and are often represented as percent volume
against dose. A cumulative DVH is useful for determining how much dose is received by
a given volume of a certain structure. Dose-volume metrics such as Dn% and Dncc, which
are the dose received by n% or n-cubic-centimeters (cc) of the volume respectively, are
found on the DVH. Also of interest are the volumes receiving nGy and n% of the
7
prescription dose, VnGy and Vn% respectively. An example of an ideal and an actual DVH
is shown in Figure 1.3.
Figure 1.3. Cumulative DVH for a target and organ at risk (OAR). An example of an
actual DVH is shown on the left, and an ideal DVH is shown on the right.
1.4 Anatomical changes during treatment
One of the challenges of radiation therapy is that anatomical changes, such as weight loss
as well as tumor and OAR growth or shrinkage, often occur during the course of
treatment. An example of a head and neck cancer patient with severe weight loss and
tumor shrinkage over the treatment course is illustrated in Figure 1.4. Such anatomical
changes, which vary from patient to patient, may cause the dose distribution to differ
from what was originally planned [12]. If the patient body no longer conforms well to
immobilization devices, setup positioning and organ motion problems arise. In some
cases, the margin defined for the PTV becomes inadequate. A modified treatment plan
with a new CT simulation may be required, increasing the treatment workflow.
Figure 1.4. CT images of a head and neck cancer patient at the start of treatment (left)
and 5 weeks into treatment (right).
0
20
40
60
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100
120
0 2 4 6 8 10 12
Pe
rce
nt
Vo
lum
e (
%)
Dose (Gy)
Target
OAR
0
20
40
60
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120
0 2 4 6 8 10 12
Pe
rce
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8
1.4.1 Effects on dose distribution
The extent to which the target coverage and OAR sparing are compromised by
anatomical changes as treatment progresses have a direct consequence on the treatment
outcome. This is particularly so for IMRT because of the high dose conformity and the
steep dose falloff outside the PTV.
In head and neck radiation therapy, several studies have shown that target and OAR
shrinkage can lead to a decreased dose to the target while increasing the dose to the OAR
[13-18]. One major concern is that the parotid glands tend to shift towards midline during
the course of treatment, which correlates with an increased parotid dose and more severe
salivary toxicity. Barker et al. [12] found that for 14 patients treated with conventional
radiation therapy or 3D-CRT, the GTVs decreased in size at a median rate of 1.8% of the
initial volume per treatment day whereas the parotid size decreased at a rate of 0.6% per
day. They noticed that the volume changed at a faster rate at the beginning of treatment
than near the end. Castadot et al. [13] found that for 10 patients treated with IMRT, the
tumor and nodal GTV volumes decreased by 3.2% and 2.1% per day, respectively. The
homolateral and heterolateral parotids shrank by 0.9% and 1.0% per day, respectively.
After 5 weeks of treatment, the GTV for the tumor moved laterally by 1.3 mm while the
parotids moved by 3.4 mm. There were also substantial increases in the doses to the
submandibular glands, oral cavity, and spinal cord. Lee et al. [15] reported an average of
15% increase in parotid dose for 10 IMRT patients as a result of parotid shrinkage. In a
study of 13 patients, Hansen et al. [14] re-calculated the original IMRT plans for 13
patients on second CT scans acquired for the purpose of replanning due to weight loss or
tumor shrinkage. They found a reduction in PTV D95% for 92% of the patients. The
maximum spinal cord dose increased for all patients while the maximum brainstem dose
increased for 85% of the patients.
In breast radiation therapy, accurate patient setup is essential because the target is close to
critical structures such as the heart and the ipsilateral lung [19]. Respiratory motion can
cause these organs to be located differently than what was originally planned. Topolnjak
et al. [20] conducted a treatment verification study for 22 left-sided breast cancer patients
and examined respiratory heart motion. They reconstructed pretreatment cone beam CT
9
(CBCT) images into 10-phase 4-D scans, and registered the heart in each breathing phase
to the planning CT. Cardiac position variations relative to the bony anatomy were seen
with average heart position errors of 3.9, 8.7, and 4.0 mm in the left-right, cranial-caudal,
and anterior-posterior directions respectively. Such variations caused an increase in the
mean heart dose while the maximum heart dose remained unaffected.
In a dosimetric study of 10 left-sided breast cancer patients comparing whole-breast 3D-
CRT with various IMRT techniques, Schubert et al. [21] found that IMRT reduced hot
spots in targets as well as the dose to the heart and ipsilateral lung. On the contralateral
side, the mean dose increased for the lung and remained the same for the breast while the
maximum dose was decreased. Another study by Zhang et al. [22] compared 3D-CRT
with IMRT for 20 patients receiving whole breast irradiation. They found that IMRT
plans gave the best target dose homogeneity and conformity while reducing the irradiated
volume of OAR in high-dose areas. However, the irradiated volume in low-dose areas
was increased.
1.5 Image-guided radiation therapy
To minimize positioning variations and ensure correct target localization, on-board
imaging (OBI) is used to image the patient in treatment position immediately before
treatment delivery. When OBI is used before every treatment fraction, the process is
known as image-guided radiation therapy (IGRT). There are various OBI modalities,
including ultrasound, MV portal imaging, kV planar imaging, kV or MV CBCT, and
MVCT.
CBCT images are reconstructed from a series of planar projection images as the x-ray
source rotates about the patient lying on a linac treatment couch. The reconstruction,
commonly done using a filtered backprojection algorithm, yields a volumetric image of
the treatment site. Based on the registration between the CBCT and the planning CT, the
patient may be shifted to ensure proper positioning prior to treatment. The image source
can be either the MV treatment beam or the kV imager mounted on the linac gantry. The
kV system is a conventional x-ray tube on a retractable arm at 90° from the treatment
beam, with a flat panel detector on another retractable arm opposite the x-ray tube [23].
10
The MV system consists of the treatment beam and the electronic portal imaging device
(EPID) that extends from the bottom of the gantry opposite the treatment beam. The kV
system has better contrast and signal-to-noise ratio per unit dose than the MV system,
yielding better soft tissue visualization. However, streaking artifacts arising from high
atomic number materials are more severe for kV images. The dose to the patient is lower
for kV CBCT (2-3 cGy/image) than for MV CBCT (5-10 cGy/image) [24]. Varian’s OBI
has several kV CBCT acquisition techniques. Full-fan techniques rotate 200° around the
patient and have a reconstructed field of view (FOV) diameter of 25 cm, whereas half-fan
techniques rotate 360° and have a reconstructed FOV diameter of 45 cm [25].
MVCT images are acquired on a helical tomotherapy unit similarly to that of a CT
scanner. The radiation source for MVCT imaging has a nominal accelerating potential of
3.5 MV [26]. The dose to the patient is 1.5-3.0 cGy/image. Images may be acquired
using one of three resolutions, namely, coarse, normal, and fine. They correspond to slice
thicknesses of 6, 4, and 2 mm, respectively. The FOV diameter is 40 cm.
1.6 Adaptive radiation therapy
Adaptive radiation therapy (ART) is the technique of adjusting the patient’s treatment
based on anatomic and/or biological changes that occur during the course of treatment
[27]. The ART process can utilize image guidance, image registration, and dose
accumulation techniques for dosimetric assessment so as to determine if a modified
treatment plan is needed.
ART is often an integral part of head and neck IMRT as there can be noticeable changes
in the patient anatomy as treatment progresses. Ahn et al. [28] conducted a prospective
study of 23 such patients who received repeat CT scans at fractions 11, 22, and 33. On
average, they found a weight loss of 8.3%, a decrease in skin separation of 10%, and a
17.2% decrease in GTV volume over the treatment course. The parotid decreased in size
by 24% around the time of the first two repeat CT scans, and the volume stabilized
towards the end of treatment. The original plan for each patient was calculated on the re-
scans and the resulting dose distributions were evaluated. They found that 65% of the
patients benefitted from replanning, which was deemed necessary when the OAR
11
constraints were not met or there was inadequate target coverage. However, they did not
find a single positional or anatomic variable that indicated the need for a replan.
Wu et al. [29] retrospectively studied 11 head and neck cancer patients to assess the
difference in planned and delivered dose, CTV to PTV margins, and optimal replanning
frequency. They found that while shrinkage had no significant dosimetric effect on
targets and most critical structures, the mean dose to the parotids increased by
approximately 10%. In this study, each patient had a planning CT as well as 6 weekly
CTs during treatment. The original plan was applied to each weekly CT and the dose re-
calculated with the original CTV to PTV margin of 5 mm and with reduced margins of 3
and 0 mm. Weekly replans were also generated from the weekly CTs to find an optimal
replanning strategy. The work showed that replanning gives improved parotid sparing
with little effect on target and other normal tissue doses, and that with reduced margins
from 5 to 0 mm and weekly replans the parotids could be spared by approximately 30%.
Although increased sparing of the parotids was seen with increased frequency of replans,
replanning more than once a week is unnecessary.
The ideal use of ART would be to adapt the treatment plan based on pretreatment images
to account for daily positional and anatomic changes while the patient is on the treatment
couch. Ahunbay et al. [30] proposed an online scheme to account for interfractional
variations that produces practically equivalent DVHs to full scope re-optimization in
approximately 10 minutes when tested for direct-aperture based IMRT on prostate and
abdomen cases. The workflow consists of (1) delineation of target and OAR on the CT of
the day by modifying the planning contours, (2) adjusting the beam/segment aperture
based on the differences between the new and planning contours using a segment
aperture morphing (SAM) algorithm, (3) computing the dose distribution, (4) optimizing
the beam/segment weights of the new apertures using a segment weight optimization
(SWO) tool, and (5) transferring the new beams/segments for delivery. They found that
for small deformations where the percentage of overlapping volume of the modified
contours with the planning contours was >80%, only the SAM algorithm was needed to
produce equivalent plans to re-optimization. However, both the SAM and SWO
processes were needed for large deformations.
12
1.7 Image registration
Image registration is used to compare images acquired at different time points and from
different imaging modalities on the same geometric reference frame so features from
images are effectively superimposed. This is used often in radiation therapy to gain more
information about a patient’s disease both prior to and during treatment. Prior to
treatment, positron emission tomography (PET) scans are registered with a CT scan to
incorporate information regarding metabolic activity of the tumor. Magnetic resonance
imaging (MRI) scans, which yield superior soft tissue contrast, can be used to delineate
tumors and identify OAR. During radiation therapy, registration of the pretreatment
verification images with the planning CT helps to ensure proper positioning and to
evaluate changes in patient geometry.
Image registration can be done using either rigid or deformable techniques. Rigid
registration has only six degrees of freedom, rotational and translational, while
deformable registration has potentially unlimited degrees of freedom. An example of a
rigid registration between the planning CT and a daily MVCT is shown in Figure 1.5.
Rigid registration will be discussed in more detail in section 2.1.1.
Figure 1.5. Image registration between
MVCT (top) and planning CT (middle).
13
Deformable image registration is useful for dose accumulation as well as automatic
contouring [31]. It makes use of a deformation map to establish the voxel-to-voxel
displacement from the reference image to the test image. There are several deformable
registration algorithms available, some of which will be discussed in section 2.1.2.
1.7.1 Dose accumulation
In the ART process, dose accumulation is needed to evaluate the dose already received
by the patient at that time during a course of treatment [32]. The accumulation of dose is
achieved using the deformation map from deformable registration. The dose is calculated
on the test image (usually the daily position verification image), and the delivered dose
distribution is then deformed to the reference image (usually the planning CT) using the
deformation map. The deformed doses from multiple days of treatment are now in the
same reference frame and are accumulated.
1.8 Purpose of thesis and outline
The aim of this thesis is to evaluate the dosimetric effects of anatomical changes and
positioning variations during treatment delivery through several retrospective studies. In
one study of patients receiving VMAT breast boost treatments, the daily dose is
calculated on pretreatment CBCT images. The goal is to investigate if the target margin
used is adequate to account for daily position variations. In a second study of patients
receiving craniospinal irradiation and head and neck IMRT using helical tomotherapy,
the daily dose is calculated on the pretreatment MVCT images. The goal is to compare
the delivered dose with the planned dose to examine the effects of interfractional
variations on treatment delivery.
This thesis is organized into five chapters. The second chapter discusses the theory
behind deformable image registration, treatment planning and dose calculation. The third
chapter details the materials and methods used in the two studies as well as some
validation of the software used to complete the study. Results of the studies will be
presented and discussed in the fourth chapter. The fifth chapter contains the conclusion,
limitations of the study, and recommendations for future work.
14
References
1. American cancer society. 2. Canadian cancer society; statistics, 2012. 3. Khan FM. The physics of radiation therapy.3rd. Philadelphia ; London: Lippincott
Williams & Wilkins; 2003. 4. Icru report 83; prescribing, recording, and reporting photon-beam intensity-modulated
radiation therapy (imrt), 2010. 5. Boyer AL, Ezzel GA ,Yu CX. Intensity-modulated radition therapy. In: Khan FM and Gerbi
BJ, eds. Treatment planning in radiation oncology, ed. Third. Philadelphia: Lippincott Williams & Wilkins, 2012;pp. 201-228.
6. Tomita N, Kodaira T, Tachibana H, et al. A comparison of radiation treatment plans using imrt with helical tomotherapy and 3d conformal radiotherapy for nasal natural killer/t-cell lymphoma. Br J Radiol 2009;82:756-763.
7. Otto K. Volumetric modulated arc therapy: Imrt in a single gantry arc. Med Phys 2008;35:310-317.
8. Mackie TR, Balog J, Ruchala K, et al. Tomotherapy. Semin Radiat Oncol 1999;9:108-117. 9. Mackie TR, Holmes T, Swerdloff S, et al. Tomotherapy: A new concept for the delivery of
dynamic conformal radiotherapy. Med Phys 1993;20:1709-1719. 10. Icru report 50; prescribing, recording, and reporting photon beam therapy, 1993. 11. Icru report 62; prescribing, recording and reporting photon beam therapy (supplement
to icru report 50), 1999. 12. Barker JL, Jr., Garden AS, Ang KK, et al. Quantification of volumetric and geometric
changes occurring during fractionated radiotherapy for head-and-neck cancer using an integrated ct/linear accelerator system. Int J Radiat Oncol Biol Phys 2004;59:960-970.
13. Castadot P, Lee JA, Geets X, et al. Adaptive radiotherapy of head and neck cancer. Semin Radiat Oncol 2010;20:84-93.
14. Hansen EK, Bucci MK, Quivey JM, et al. Repeat ct imaging and replanning during the course of imrt for head-and-neck cancer. Int J Radiat Oncol Biol Phys 2006;64:355-362.
15. Lee C, Langen KM, Lu W, et al. Assessment of parotid gland dose changes during head and neck cancer radiotherapy using daily megavoltage computed tomography and deformable image registration. Int J Radiat Oncol Biol Phys 2008;71:1563-1571.
16. O'Daniel JC, Garden AS, Schwartz DL, et al. Parotid gland dose in intensity-modulated radiotherapy for head and neck cancer: Is what you plan what you get? Int J Radiat Oncol Biol Phys 2007;69:1290-1296.
17. Robar JL, Day A, Clancey J, et al. Spatial and dosimetric variability of organs at risk in head-and-neck intensity-modulated radiotherapy. Int J Radiat Oncol Biol Phys 2007;68:1121-1130.
18. Han C, Chen YJ, Liu A, et al. Actual dose variation of parotid glands and spinal cord for nasopharyngeal cancer patients during radiotherapy. Int J Radiat Oncol Biol Phys 2008;70:1256-1262.
15
19. Yoo S, Ma J, Marks L, et al. Breast cancers. In: Timmermam R and Xing L, eds. Image-guided and adaptive radiation therapy. Philadelphia: Lippincott Williams & Wilkins, 2010;pp. 202-215.
20. Topolnjak R, Borst GR, Nijkamp J, et al. Image-guided radiotherapy for left-sided breast cancer patients: Geometrical uncertainty of the heart. Int J Radiat Oncol Biol Phys 2012;82:E647-E655.
21. Schubert LK, Gondi V, Sengbusch E, et al. Dosimetric comparison of left-sided whole breast irradiation with 3dcrt, forward-planned imrt, inverse-planned imrt, helical tomotherapy, and topotherapy. Radiother Oncol 2011;100:241-246.
22. Zhang F ,Zheng M. Dosimetric evaluation of conventional radiotherapy, 3-d conformal radiotherapy and direct machine parameter optimisation intensity-modulated radiotherapy for breast cancer after conservative surgery. J Med Imaging Radiat Oncol 2011;55:595-602.
23. Jaffray DA, Siewerdsen JH, Wong JW, et al. Flat-panel cone-beam computed tomography for image-guided radiation therapy. Int J Radiat Oncol Biol Phys 2002;53:1337-1349.
24. Bujold A, Craig T, Jaffray D, et al. Image-guided radiotherapy: Has it influenced patient outcomes? Semin Radiat Oncol 2012;22:50-61.
25. Ueltzhoffer S, Zygmanski P, Hesser J, et al. Clinical application of varian obi cbct system and dose reduction techniques in breast cancer patients setup. Med Phys 2010;37:2985-2998.
26. Meeks SL, Harmon JF, Jr., Langen KM, et al. Performance characterization of megavoltage computed tomography imaging on a helical tomotherapy unit. Med Phys 2005;32:2673-2681.
27. Yu CX. Delivery of intensity-modulated radiation therapy. In: Li XA, ed. Adaptive radiation therapy. Boca Raton, FL: CRC Press, 2011;pp. 127-139.
28. Ahn PH, Chen CC, Ahn AI, et al. Adaptive planning in intensity-modulated radiation therapy for head and neck cancers: Single-institution experience and clinical implications. Int J Radiat Oncol Biol Phys 2011;80:677-685.
29. Wu Q, Chi Y, Chen PY, et al. Adaptive replanning strategies accounting for shrinkage in head and neck imrt. Int J Radiat Oncol Biol Phys 2009;75:924-932.
30. Ahunbay EE, Peng C, Chen GP, et al. An on-line replanning scheme for interfractional variations. Med Phys 2008;35:3607-3615.
31. Lu W, Olivera GH, Chen Q, et al. Deformable registration of the planning image (kvct) and the daily images (mvct) for adaptive radiation therapy. Phys Med Biol 2006;51:4357-4374.
32. Janssens G, de Xivry JO, Fekkes S, et al. Evaluation of nonrigid registration models for interfraction dose accumulation in radiotherapy. Med Phys 2009;36:4268-4276.
16
Chapter Two
Theory
Contents
2.1 Image registration algorithms……………………. 16
2.1.1 Rigid registration……………………………… 17
2.1.2 Deformable registration………………………. 17
2.1.3 MIM Software….……………………………… 19
2.1.4 Dose deformation……………………………… 19
2.2 Similarity measures…………………....................... 20
2.3 Treatment planning process…..…………………… 21
2.3.1 Forward and inverse planning…………………. 21
2.3.2 Dose calculation algorithms………………….. 22
2.3.3 Tissue inhomogeneity corrections…………….. 22
Commercially available image registration, deformation and dose accumulation software
as well as commercially available dose calculation algorithms were used throughout the
work covered in this thesis. Deformable registration algorithms and the similarity
measures used to evaluate them will be discussed in sections 2.1 and 2.2. Treatment
planning and dose calculation algorithms will be discussed in section 2.3, as well as the
relationship between Hounsfield Units (HU) and electron density, which is necessary for
tissue inhomogeneity corrections in dose calculations.
2.1 Image registration algorithms
The registration of images involves mapping a transformation between the two images;
an ideal transformation will perfectly align the test image to the reference image. This
relationship can be expressed as
(2.1)
17
where T is the spatial transformation between the test image, F, and reference image, R.
There are two classes of image registration algorithms: feature-based and greyscale
image-based. Feature-based algorithms use contours, fiducial markers, and landmark
points common in the two images for registration. These features are either defined
manually or automatically. The algorithm tries to minimize the distance between these
features. Image-based algorithms use pixel or voxel based information, relying on the
varying intensities within the image for registration [1].
In radiation therapy, image registration is performed with the verification images and the
planning CT by transforming the verification image onto the reference images’ frame of
reference. Normally, this comparison is achieved using a rigid registration; however, it is
beneficial when using these images for the purpose of adaptive radiation therapy (ART)
to be able to apply deformable registration techniques. Deformable registration is able to
account for the changing soft tissue anatomy often seen in patients receiving radiation
therapy such as tumor and normal tissue shrinkage where a rigid registration only
accounts for positional variations.
2.1.1 Rigid registration
A rigid registration can be applied to either the entire image or to a specified region of
interest. Distances between pairs of points in the original images are preserved and results
in objects retaining their original shape and size after rigid registration [2]. Rigid
registration allows for the registration of two images using six degrees of freedom, three
rotational and three translational. The image sets are effectively superimposed and
“shifted” until a best fit is found.
2.1.2 Deformable registration
Unlike rigid registration, deformable registration allows for individual voxels to be
mapped independently. Deformable registration algorithms determine a deformation map
from the test image to the reference image and use the map to deform the image on a
voxel-to-voxel basis. The deformation map can also be used to deform doses as well as
contours associated with an image.
18
By using an affine transformation which accounts for scaling and shearing, twelve
degrees of freedom are now achievable in the deformation. However, this accounts for
only the global registration, not the local deformation. One method to account for the
local deformation is free-form deformation (FFD) based on B-splines [3]. FFD uses a
mesh of control points to perform the local deformation. The control points ϕi, j, k have a
uniform spacing δ and when manipulated the image is deformed by:
∑ ∑ ∑
(2.2)
where ⌊ ⁄ ⌋ ⌊ ⁄ ⌋ ⌊ ⁄ ⌋ ⁄ ⌊ ⁄ ⌋
⁄ ⌊ ⁄ ⌋ ⁄ ⌊ ⁄ ⌋ and where Bl represents the lth basis function of
the B-spline,
⁄ (2.3)
⁄ (2.4)
⁄ (2.5)
⁄ (2.6)
B-splines are locally controlled, and therefore changing the location of one point only
affects the deformation transformation in that region. This makes this an efficient
deformation technique even when many control points are used [3].
A voxel similarity based free-form deformable registration method developed by Lu et al.
models the deformable registration problem as a functional minimization problem. The
smoothness of the displacement field is used as a constraint, and the optimization
addresses the trade-off between similarity measures and smooth constraints [4]. The
position of any point in the body with respect to a reference configuration is used to
describe the deformation of the body. A displacement vector is used to describe the
change in position of each point at a given time. Finding the displacement vectors is the
objective of deformable registration in order to map the displacement from the reference
19
domain to the test domain. In FFD there are 3N degrees of freedom, where N is the total
number of voxels.
2.1.3 MIM Software
An intensity based free-form technique is used by the MIM Software (MIM Software,
Inc., Cleveland, OH) for deformable registration. The algorithm uses essentially limitless
degrees of freedom, allowing the registration to account for both local deformations as
well as global changes [5]. As an evaluation of the algorithm, Piper compared the
correlation coefficient after rigid and deformable registration of CT volume pairs for
three patients with significant weight loss. In addition, a known deformation was applied
to a real CT volume to test the ability of the algorithm to achieve the same deformation.
The correlation coefficients for the three CT volume pairs increased when deformable
registration techniques were used. Using deformable registration, 73.9% of the voxels
had errors less than 1 mm and the 95% confidence interval was 4.8 mm compared to
0.6% and 23.2 mm with rigid registration.
2.1.4 Dose deformation
Deformable registration algorithms are capable of deforming contours and dose
associated with the image being deformed. However, validation of the deformed dose is
somewhat of a challenge. When deforming an image and the contours associated with it,
one can visually inspect the deformed image for any obvious errors in the deformation.
The contours, if deformed properly, will align with the organs on the new image. This is
not the same when looking at dose as there is no dose on the new image to compare the
deformed dose distribution to. In an evaluation of a deformable re-contouring method,
Fragoso et al. found that the automatic algorithm generated less volume differences for
unchanging structures than modified or manually drawn contours [6]. The study included
seven head and neck cancer patients each with two CT sets, contours were manually
drawn on each CT. Using the MIM Software’s deformable re-contouring tool, the
contours from the initial CT were deformed to the re-planning CT and modifications
were made to the automatic contours as needed. The consistency between the sets of
contours was evaluated using the overlap metric defined as the ratio of the set intersection
20
to the set union. The automatic re-contouring method with some modification was able to
produce treatment ready contours in significantly less time than manual contouring (~1
hour versus 3-4.5 hours). In the evaluation of unchanging structures (brainstem,
cerebellum, and spinal cord) they found the volume difference from the original CT to be
3.94% for the automatic contours versus 8.27% and 15.40% for the modified and manual
contours respectively.
2.2 Similarity measures
Similarity measures are a metric of how well two images match. In intensity based
registration algorithms, the aim is to find the transformation that yields the optimal
similarity measure, meaning that after registration the test image is most similar to the
reference image. Common similarity measures used are the sum of squared distance
(SSD), cross correlation (CC), and mutual information (MI).
The simplest similarity measure is the SSD which measures the difference in intensities
between the images. This method is used for mono-modal images that differ only by
Gaussian noise [7]. SSD is defined as:
∑( ( ))
(2.7)
where T(x) is the intensity at a position x in an image and S(t(x)) is the intensity at the
corresponding point (in the test image) given by the current estimate of the
transformation t(x). N is the number of voxels in the region of overlap.
Another similarity measure used for mono-modal images is CC, which assumes that the
intensities in the images have a linear relationship. CC is defined as:
∑ ( ( ) )
√∑ ∑( ( ) )
(2.8)
where and are the mean intensity of the reference and test image respectively. If the
reference and test image are identical then CC will be unity, a perfect registration was
achieved.
21
For multi-modal image registration, a common similarity measure is MI. It assumes a
probabilistic relationship between voxel intensities of the two images. MI is a
relationship between the amount of information contained in one image about the other.
In terms of entropies of the intensity distribution, MI is defined as:
(2.9)
with
∑
(2.10)
∑
(2.11)
∑
(2.12)
where P or Q are the probability of intensity I or J (respectively) occurring in the
reference or test image and pij is the joint probability of both occurring in the same place.
The larger the MI, the better the registration is.
2.3 Treatment planning process
Treatment plans are often created using kV CT images acquired with the patient in the
treatment position on a CT simulator. A CT scan is used as it provides three dimensional
information regarding patient anatomy, geometry, and tissue densities. Once the patient
image is acquired, targets and organs at risk (OAR) are defined according ICRU
recommendations discussed in section 1.3 and a treatment is prescribed by the physician.
The treatment planning system (TPS) calculates the dose distribution within the patient
based on parameters defined by the treatment planner. There are two types of treatment
planning approaches: forward and inverse planning.
2.3.1 Forward and inverse planning
Forward planning allows the planner to select the beam energy, beam directions and
weights, field widths, and intensity modifiers to obtain an optimal plan. The TPS then
calculates the dose distribution based on these parameters and adjustments may be made
22
by the planner as necessary. Inverse planning is an iterative cost function based process
where each field is divided into beamlets whose weights and intensities are adjusted by
the TPS in an attempt to meet the dose distribution criteria defined by the planner. During
the iterative process, the planner may adjust the dose-volume constraints.
2.3.2 Dose calculation algorithms
There are three broad categories of dose calculation algorithms: correction based, model
based, and Monte Carlo simulation based [8]. Both the model based and Monte Carlo
based algorithms can simulate radiation transport in three dimensions (3D), which allows
for a more accurate dose calculation in a 3D volume. Correction based algorithms are
based on measured data such as percent depth dose curves and beam profiles.
An example of a model based algorithm is the convolution-superposition method.
Convolution-superposition takes into consideration the contribution from primary
photons as well as that from scattered photons and electrons resulting from primary
photon interactions. The convolution-superposition equation is given by:
∫ ( ) (2.13)
where is the radiologic path length from the source to the primary photon
interaction site, is the radiologic path length from the site of primary
photon interaction to the site of dose deposition, ( ) is the dose kernel,
and is the terma which is the product of mass attenuation coefficient and the
primary energy fluence [8]. Varian’s (Varian Medical Systems, Palo Alto, CA) Eclipse
TPS and TomoTherapy’s (TomoTherapy/Accuray, Madison, WI) HiArt TPS both use
convolution-superposition algorithms.
2.3.3 Tissue inhomogeneity corrections
The relationship between electron density and HU is necessary for tissue heterogeneity
corrections to be used accurately in treatment planning [9]. This relationship can be
determined by scanning a phantom containing regions of known densities, or tissue
substitutes.
23
The presence of an object in a CT scanner causes attenuation of the beam that reaches the
detector. This attenuation can be quantified using the average linear attenuation
coefficient, µ, along that path. The resulting reconstructed image is made up of a 3D
matrix of HU that can be defined as follows [10]:
(2.14)
where µm is the linear attenuation coefficient of a given material, and µwater is the linear
attenuation coefficient for water. HU values range from around -1,000 to +3,000. A HU
of -1,000 and 0 correspond to air and water respectively, and +1,000 to bone.
The relationship between HU and electron density depends on the scanning technique and
can be CT scanner specific. It should be measured for the scanner acquiring the images
used for treatment planning to ensure the appropriate relationship is used [9]. The HU-to-
density conversion curves for kV CT, MVCT, and CBCT acquired with different
scanning techniques are shown in Figure 2.1.
Figure 2.1. HU calibration curves for kV CT, MVCT, and CBCT images.
While the curve for a CT simulator is quite stable over time, the MVCT curve is not. It
has been seen that calculation using different MVCT curves on the same image can give
differences in dose of 3% [11]. Therefore, the correct HU calibration curve is necessary
for dose calculation.
-1500
-1000
-500
0
500
1000
1500
0 0.5 1 1.5 2Me
an H
U
Density [ g/cm3 ]
kV CT
MVCT
kV CBCT
24
References
1. Wang H, Dong L, O'Daniel J, et al. Validation of an accelerated 'demons' algorithm for deformable image registration in radiation therapy. Phys Med Biol 2005;50:2887-2905.
2. Brown LG. A survey of image registration techniques. ACM Computing Surveys (CSUR) 1992;24:325-376.
3. Rueckert D, Sonoda LI, Hayes C, et al. Nonrigid registration using free-form deformations: Application to breast mr images. IEEE Trans Med Imaging 1999;18:712-721.
4. Lu W, Chen ML, Olivera GH, et al. Fast free-form deformable registration via calculus of variations. Phys Med Biol 2004;49:3067-3087.
5. Piper J. Evaluation of an intensity-based free-form deformable registration algorithm. Med Phys 2007;34:2353-2354.
6. Fragoso R, Piper J, Nelson A, et al. Evaluation of a deformable re-contouring method for adaptive therapy. ACRO Annual Meeting. 2008
7. Crum WR, Hartkens T ,Hill DL. Non-rigid image registration: Theory and practice. Br J Radiol 2004;77 Spec No 2:S140-153.
8. Khan FM. The physics of radiation therapy.3rd. Philadelphia ; London: Lippincott Williams & Wilkins; 2003.
9. Constantinou C, Harrington JC ,DeWerd LA. An electron density calibration phantom for ct-based treatment planning computers. Med Phys 1992;19:325-327.
10. Brooks RA. A quantitative theory of the hounsfield unit and its application to dual energy scanning. J Comput Assist Tomogr 1977;1:487-493.
11. Langen KM, Meeks SL, Poole DO, et al. The use of megavoltage ct (mvct) images for dose recomputations. Phys Med Biol 2005;50:4259-4276.
25
Chapter 3
Materials and Methods
Contents
3.1 MIM deformation validation……………………. 26
3.2 Hounsfield Unit to density calibration…………. 27
3.3 Breast boost dose verification study……………... 28
3.3.1 Patients…………………………………….. 28
3.3.2 Daily imaging……………………………. 29
3.3.3 Merged image……………………………… 30
3.3.4 Daily dose calculations…………………. 31
3.3.5 Dose deformation and accumulation …….. 31
3.4 Planned Adaptive treatment verification study…. 32
3.4.1 Craniospinal irradiation patients…………… 32
3.4.2 Head and neck patient……………………… 33
3.4.3 Helical TomoTherapy treatment delivery… 33
3.4.4 Planned Adaptive module..………………… 33
In this chapter, the details of the software validation and patient dose verification studies
done for this thesis work will be discussed.
All patients were treated at the Montreal General Hospital in Montreal, Quebec. Original
treatment plans were calculated on kilovoltage (kV) CT images obtained on the Philips
Brilliance CT Big Bore (Philips Electronics, Markham, ON) CT simulator using 120 kVp
and 3 mm slice thickness. For each patient, the prescription dose was given to the
planning target volume (PTV) such that 95% of the volume received 100% of the
prescription dose.
26
3.1 MIM deformation validation
The MIM Software (MIM Software Inc., Cleveland, OH) deformable registration tools
were used to deform dose and structure contours throughout this work. As a validation of
the accuracy of the deformation, contours for several structures were deformed and
compared with previously drawn contours. Contours for the lungs, heart and spinal cord
were drawn in Varian’s Eclipse treatment planning system (TPS) on each pretreatment
CBCT image for two breast cancer patients. The contours were deformed from the CBCT
to the planning CT in MIM using the Adaptive Re-contour Deformable workflow. The
CBCT was first rigidly registered and then deformed to the planning CT. The
deformation vector field was then applied to the contours associated with the CBCT.
Each deformed structure set was compared with the original structure set for the planning
CT. The similarity measures used to evaluate the accuracy of the deformation included
the Dice coefficient, sphere equivalent radius, and centroid location.
The Dice coefficient (D) is used to evaluate how similar two objects are, in this case
volumes of drawn contours. The Dice coefficient will range from 0 to 1, with 1 indicating
that the objects are identical. The Dice coefficient is obtained by dividing twice the
intersection by the sum of the volumes, as expressed below [1].
| |
| | | |,
(3.1)
where A and B represent the two volumes being compared. The sphere equivalent radius,
r, is the radius of a sphere with the same volume to that of the contoured object. It is
expressed as follows:
√
(3.2)
where V is the volume of the object. The centroid is the geometric center of the contour.
As another validation of the dose deformation, the dose was calculated on daily MVCT
images for a head and neck patient (receiving 2 Gy/fraction for 35 fractions) using
TomoTherapy’s (TomoTherapy/Accuray, Madison, WI) Planned Adaptive software. The
27
images as well as dose distribution were imported into MIM. The spinal cord was
contoured on each of the MVCT images and a planning organ at risk volume (PRV) was
created using a 5 mm margin. The dose from each day of treatment as calculated on the
MVCT was deformed to the planning CT using the Dose Accumulation Deformable
workflow. The near maximum dose (D2%) to the spinal cord and spinal cord PRV as
calculated on both image sets were compared.
3.2 Hounsfield Unit to density calibration
Tissue densities derived from Hounsfield Units (HU) are used by the TPS for tissue
inhomogeneity corrections. Also, having a HU-to-density conversion will help to deform
images acquired with different scanning techniques in MIM. In this work, the Gammex
TomoPhant tissue characterization phantom (Gammex, Middleton, WI) was used to
create the conversion curves for MVCT and CBCT images. The kVCT conversion curve
used was the one currently being used in our clinic. The phantom contains 12 calibrated
rods of known densities ranging from 0.33 g cm-3
(lung, LN-300) to 1.824 g cm-3
(cortical bone). The rods were distributed throughout the phantom as shown in Figure
3.1. The phantom was imaged on TomoTherapy using normal and course imaging modes
(MVCT) and on the Varian Clinac 2100 using the Low Dose Thorax protocol (CBCT,
110 kVp, 20 mA, 20 ms, half fan scan, half bowtie filter, 360° gantry rotation [2]). The
HU values were recorded for 5 slices near the center of each rod. The average reading
was then taken to be the HU for the density of that rod.
28
Figure 3.1. Tissue characterization phantom used for the
derivation of the Hounsfield unit-to-density conversion curve.
3.3 Breast boost dose verification study
In this study, pretreatment CBCT images were used to calculate the daily dose received
by breast cancer patients undergoing a volumetric modulated arc therapy (VMAT). The
daily doses were deformed and accumulated on the planning CT. The purpose was to
evaluate if differences between the actual treatment and the CT simulation positions
would have any dosimetric effects. The workflow, as shown below in Figure 3.2, will be
explained in the sections that follow.
Figure 3.2. Workflow for breast boost dose verification study.
3.3.1 Patients
Four patients were retrospectively studied. They underwent breast boost VMAT
treatment in which daily CBCT scans were acquired for positioning verification. They
were positioned head first supine with arms up using a wingboard, headrest, and knee
sponge.
Original Plan on CT
CBCT Merged
Image in MIM
Calculated Dose in Eclipse
Deform Dose in MIM
Accumulate Dose in MIM
Plan Comparison
29
All patients had contours drawn for the tumor bed, PTV, heart, right and left lungs, spinal
cord, and carina. The margin used for the PTV based on the tumor bed was 1 cm. All
VMAT plans were optimized using Varian’s (Varian Medical Systems, Palo Alto, CA)
Progressive Resolution Optimizer (Version 8.6.15). The dose was calculated using the
analytic anisotropic algorithm (AAA) [3], with inhomogeneity corrections turned off
using 6 MV photons. Dose prescriptions and beam delivery parameters are shown in
Table 3.1.
Patient Site Prescription
/Fractions
Arc Gantry
Rtn.
(deg)
Stop
Angle
(deg)
Coll.
Rtn.
(deg)
Field
X
(cm)
Field
Y
(cm)
MU
1 Left
breast
10 Gy
/ 5
1 310 165 30 7.8 6.8 234
2 165 310 330 7.7 6.5 234
2 Right
breast
12.5 Gy
/ 5
1 30 200 30 6.7 7.6 398
2 200 30 330 7.8 7.1 411
3 Right
breast
10 Gy
/ 4
1 190 40 30 6.9 6.0 419
2 40 190 330 6.9 6.1 390
4 Left
breast
10 Gy
/4
1 320 150 330 10.3 8.6 497
2 150 320 30 9.5 8.9 525
Table 3.1. Breast patient prescription and field characteristics for the four VMAT plans.
3.3.2 Daily imaging
Patients were set up on the treatment couch based on marks defined during CT
simulation. Varian’s kV CBCT on-board imager (OBI) was used to acquire images using
the low-dose thorax protocol. The CBCT was rigidly registered with the planning CT
using the console software, and shifts were applied as needed to ensure correct
positioning. Shifts applied to the patients in this study are shown in Table 3.2.
30
Fraction 1 Fraction 2 Fraction 3 Fraction 4 Fraction 5
Patient Vt Lg Lt Vt Lg Lt Vt Lg Lt Vt Lg Lt Vt Lg Lt
1 0.0 0.0 0.0 -0.2 0.0 0.3 -0.5 0.1 0.1 -0.1 0.1 0.4 0.1 0.1 0.4
2 0.0 0.0 0.5 0.2 -0.5 0.4 0.0 0.0 0.0 0.4 -0.5 0.1 0.1 0.0 0.5
3 -0.4 0.1 0.2 0.0 -0.5 0.6 -0.2 -0.6 0.1 0.1 -0.4 -0.1
4 0.8 -0.1 -0.1 0.1 -0.2 -0.2 -0.2 0.3 -0.1 0.0 0.0 0.0
Table 3.2. Daily shifts applied to patient prior to treatment based on CBCT image verification.
All shifts are in units of centimeters (cm). Vertical (Vt) indicates the anterior-posterior axis
where positive is in the anterior direction. The longitudinal (Lg) axis is positive in the superior
direction, and the lateral (Lt) axis is positive to the left.
3.3.3 Merged image
For the purpose of verification dose calculation, a merged image was first created using
the CBCT and the planning CT. This was done because the CBCT often has portions of
the patient missing in the lateral direction due to the limited field of view (45 cm), as well
as being limited in the superior and inferior directions due to the limited longitudinal
length of the scan (15 cm).
Using the MIM Software, the merged image was created by first rigidly registering the
two images and saving the registered CBCT image with the same resolution as that of the
CT. The two images were now on the same coordinate system and of the same voxel
resolution. Next, the CBCT image was effectively inserted into the planning CT image.
This allowed for a complete image set while maintaining the daily geometry in the target
region. An example of a merged image is shown in Figure 3.3.
Figure 3.3. Merged image of CBCT and CT for a breast cancer patient created in MIM.
CT
CBCT
CBCT
CT
31
3.3.4 Daily dose calculations
The merged image was imported into Varian’s Eclipse TPS for dose calculation. For each
day, the dose was calculated on the merged image using the same beam delivery
configuration and dose calculation settings as the original VMAT plan.
Calculations were done with two different isocenter positions corresponding to: (1) the
initial patient setup and (2) the shifts applied to the patient prior to treatment based on
CBCT verification. The isocenter positions on the merged image were set according to
the DICOM offset and isocenter shift of the original planning CT image. In the second
case, an additional shift was applied based on the pretreatment CBCT verification. All
calculations were done without heterogeneity corrections so as to be consistent with the
original plan. Figure 3.4 shows a CBCT image with the VMAT beams on the left and the
resulting dose distribution on the right.
Figure 3.4. VMAT beams on CBCT image and the corresponding dose colorwash for
a prescription dose of 2.5 Gy per fraction.
3.3.5 Dose deformation and accumulation
The daily dose distributions were deformed and accumulated onto the planning CT using
MIM. First, the Dose Accumulation Deformable workflow was used. It involved an initial
rigid registration using a box-based assisted alignment, which allowed the user to define a
32
region of interest around the tumor site so as to achieve a better preliminary match within
that region. The CBCT image was then deformed to the planning CT. The same
deformation was applied to the dose distribution and contours as well.
All deformed daily doses were accumulated on the planning CT using the Dose
Accumulation Boost workflow. A voxel-by-voxel dose summation was done in this step.
The distributions were then compared with the original plan. Target coverage as well as
doses to the heart and lungs were evaluated.
3.4 Planned Adaptive treatment verification study
The aim of this dose accumulation study involving six helical tomotherapy (HT) patients
is to investigate the dosimetric effects of anatomical changes throughout treatment. The
workflow is shown in Figure 3.5. Each step will be explained in the sections following.
Figure 3.5. Workflow for Planned Adaptive treatment verification study.
3.4.1 Craniospinal irradiation patients
Five craniospinal irradiation (CSI) patients were retrospectively studied. Their diagnosis,
prescription as well as weight loss during treatment are shown in Table 3.3. Patients were
positioned head first supine and immobilized using standard head and neck thermoplastic
masks. The clinical target volume (CTV), consisting of the whole brain and spinal cord,
as well as organs at risk (OAR) were contoured for each patient. All treatment plans used
a field width of 5 cm.
Original Plan on CT
MCVT Merged Image
in Planned Adaptive
Calculate on Merged Image
in Planned Adaptive
Deform Dose in MIM
Accumulate Dose in MIM
Plan Comparison
33
Patient Diagnosis Prescription Age Gender Weight Loss
1 Germinoma 30 Gy in 20 fractions 12 Male 2.0 kg
2 Medulloblastoma 36 Gy in 20 fractions 34 Female 3.0 kg
3 Medulloblastoma 36 Gy in 20 fractions 17 Male *
4 Germ cell tumor 36 Gy in 20 fractions 16 Male 4.1 kg
5 Germ cell tumor 36 Gy in 20 fractions 18 Female 2.6 kg
Table 3.3. CSI patient diagnosis and prescription.
*Weight loss was not documented for patient 3.
3.4.2 Head and neck patient
One head and neck patient treated for base of tongue squamous cell carcinoma was
studied. The patient was a 60-year-old male who experienced an 8.5 kg weight loss over
the course of treatment. A standard thermoplastic mask was used for immobilization in
the head first supine position. The treatment plan used a field width of 2.5 cm. The plan
included three PTVs with prescription doses of 70, 63 and 56 Gy over 35 fractions
(PTV70, PTV63, and PTV56 respectively). Standard OAR limits were used and dose was
minimized to the OAR without reducing coverage of the targets.
3.4.3 Helical TomoTherapy treatment delivery
Patients treated on HT undergo a MVCT scan prior to every treatment fraction. The
MVCT is rigidly registered with the planning CT using the console software, and shifts
are applied to the patient as needed before treatment.
3.4.4 Planned Adaptive module
The Planned Adaptive software was used to calculate the dose for each fraction based on
the daily MVCT images. It created a merged image according to a rigid registration
between the MVCT and the planning CT done by the therapists. The MVCT image was
then inserted into the planning CT, replacing the portion of the CT contained in the
MVCT [4]. The merged image was used to calculate a verification dose based on the
treatment sinogram delivered that day. The sinogram is a 2D representation of the fluence
pattern used to deliver the treatment [5]. An appropriate HU-to-density conversion was
applied. The verification and planned isodose distributions for a CSI patient are shown in
Figure 3.6.
34
Figure 3.6. Planned (solid) and verification (dashed) dose isodose lines shown on a
merged image for a CSI patient.
The verification doses were exported to MIM for deformation and accumulation onto the
planning CT. Structures of interest for the CSI study are the CTV, heart, kidneys, and
lungs. The PTV, parotids, and spinal cord are of interest for the head and neck study.
35
References
1. Dice LR. Measures of the amount of ecologic association between species. Ecology 1945;26:297-302.
2. Ding GX ,Coffey CW. Beam characteristics and radiation output of a kilovoltage cone-beam ct. Phys Med Biol 2010;55:5231-5248.
3. Sievinen J, Ulmer W ,Kaissl W. Aaa photon dose calculation model in eclipse Palo Alto, CA: Varian Medical Systems, 2005.
4. Tomotherapy hi-art system; planned adaptive guide; 2007. 5. Kapatoes JM, Olivera GH, Reckwerdt PJ, et al. Delivery verification in sequential and
helical tomotherapy. Phys Med Biol 1999;44:1815-1841.
36
Chapter Four
Results and Discussion
Contents
4.1 Validation of MIM Software…....................................... 36
4.1.1 Contour deformation………………………………. 36
4.1.2 Dose deformation………………………………….. 38
4.2 Breast boost study……………………………………… 39
4.3 Planned Adaptive treatment verification study……….… 44
4.3.1 Craniospinal irradiation study………………..….... 44
4.3.2 Head and neck study……………………………….. 48
4.4 Limitations………………………………………………. 51
This chapter will present the deformation validation section of this work in the first
section. The two sections that follow will present and discuss the results for the breast,
craniospinal irradiation (CSI), and head and neck studies.
4.1 Validation of MIM Software
The results of the contour and dose deformation validation of the MIM Software are
presented separately in this section.
4.1.1 Contour deformation
The results for the contour deformation validation for the breast patients are shown in
table 4.1. The delta term is the mean change in value for all the deformations from the
original structure contour. The values shown for the Dice coefficient are the mean
coefficient for all the deformed contours and the standard deviation. The Dice coefficient
values range from 0.74-0.94, suggesting an acceptable overlap of the deformed and
original contours. In an evaluation of their automatic segmentation algorithm, Zhang et
al. found agreement between manually drawn contours and those generated using the
auto-segmentation with a Dice coefficient of approximately 0.8 for most regions of
37
interest [1]. The largest difference in sphere equivalent radius (SER) is 0.22 cm,
indicating good agreement with the volumes of the deformed contours as this corresponds
to a change in volume of 0.04 cm3. The centroid location for the lung contours was within
1.8 mm in any direction of the original contour, while the heart and spinal cord agreed to
within 5 mm. The results could be user dependent as the contours on the cone beam CT
(CBCT) were not drawn by the same person who contoured the original CT. Zhang et al.
found the distance transformation between the surfaces of deformed and reference
contours to be within 3 mm [1]. The heart is also subject to motion artifacts arising from
its own beating and respiratory motion. If the extent of the spinal cord contour is not the
same in the superior-inferior (z) direction for all structure sets this could introduce some
error in the centroid location. More study would be needed to eliminate the user
variability in the contour deformation results. However, these results give us sufficient
confidence in the deformation for the purpose of this study.
Centroid X
(mm)
Centroid Y
(mm)
Centroid Z
(mm)
SER
(cm)
Dice
coefficient
Left Lung Delta 0.74 0.55 1.75 0.16 0.94
Std. Dev. 0.45 0.62 0.56 0.05 0.00
Right Lung Delta 0.29 0.73 1.42 0.13 0.94
Std. Dev. 0.24 0.60 0.64 0.06 0.01
Heart Delta 5.00 1.60 2.40 0.22 0.86
Std. Dev. 2.50 1.60 2.30 0.13 0.02
Spinal Cord Delta 0.35 2.23 4.53 0.04 0.74
Std. Dev. 0.72 2.06 2.95 0.06 0.05
Table 4.1. Contour deformation validation values. SER = sphere equivalent radius, Std. Dev. = standard
deviation. X indicates left-right direction, Y indicates posterior-anterior direction, and Z indicates superior-
inferior direction.
A study by Nelms et al. found significant inter-clinician variability in the contouring of
organs at risk for a head and neck patient [2]. For the same CT dataset contoured by 32
clinicians, the brainstem, parotid glands and spinal cord were found to be the most
sensitive to variations, while the brain and mandible were the least sensitive. The quality
of the contours is also important to achieve an accurate deformable registration. For the
registration to track the changes in shape, the original contour should perfectly define the
edge of the organ with the largest change in intensity [3]. The quality of the CT, motion
artifacts, contouring skill and inter-observer variability do not always make this a
practical reality. In an evaluation of an automatic segmentation algorithm involving nine
38
lung patients, Wang et al. found that the tumor and surrounding tissue had distorted
shapes and volumes at mid-inspiration and mid-expiration due to irregular breathing
during the 4D acquisition [3]. Their study also included eight head and neck patients and
one prostate patient. Contours were mapped from the planning CT to the daily CT and
evaluated against physician drawn contours. They found a mean volume overlap index of
83% and a mean absolute-surface-to-surface distance of 1.3 mm.
4.1.2 Dose deformation
The ratio of the near maximum dose (D2%) to the spinal cord per fraction as calculated
using the Planned Adaptive software and then deformed to the planning CT is shown in
Figure 4.1. The dashed line indicates the mean ratio of 0.99 with a standard deviation of
0.01. We would expect that the dose to the spinal cord as calculated with the Planned
Adaptive software would remain the same when deformed to the planning CT since the
spinal cord is not changing much over the course of treatment. A perfect registration and
deformation would show the same dose to the spinal cord after deformation back to the
planning CT as that calculated in the Planned Adaptive software.
Figure 4.1 Ratio of near maximum dose to spinal cord as calculated on the MVCT in
Planned Adaptive to the deformed dose for each fraction.
The near maximum dose to the spinal cord and spinal cord PRV for each fraction are
shown in Figure 4.2. The dose calculated on the daily MVCT using Planned Adaptive is
compared with that deformed to the planning CT.
0.9
0.95
1
1.05
1.1
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
MV
CT
do
se/D
efo
rmed
do
se
Fraction
39
Figure 4.2. Near maximum dose to the spinal cord and spinal cord PRV for each fraction
as calculated on the MVCT in Planned Adaptive and also deformed to the original CT in
MIM.
The mean spinal cord D2% as calculated on the MVCT and deformed to the planning CT
are 0.77 ± 0.01 Gy and 0.78 ± 0.01 Gy respectively. The mean spinal cord PRV D2% are
0.94 ± 0.02 Gy and 0.93 ± 0.02 Gy for the MVCT and deformed doses respectively. In
the original treatment plan, the spinal cord D2% was 0.75 Gy whereas the spinal cord PRV
D2% was 0.95 Gy. Such small differences give validity to the robustness of the
deformable registration algorithm. It is possible to use the same deformable registration
method to transform the dose distribution back to the planning CT [4]. There have been
several studies using the deformable image registration tools available in the MIM
software for the deformation of dose as well as contours [5-7].
For the purpose of this work we can use the MIM Software for dose and contour
deformation with confidence given that electron density calibration curves are used when
applying deformations on images of different modalities.
4.2 Breast boost study
The dose volume statistics for the four breast cancer patients are shown in tables 4.2 and
4.3 for the dose calculated on the CBCT images with the isocenter in the shifted
treatment position and the un-shifted initial set-up position respectively. The statistics
0.74
0.79
0.84
0.89
0.94
0.99
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Do
se [
Gy
]
Fraction
D2%
MVCT Spinal Cord
Deformed Spinal
Cord
MVCT spinal cord
PRV
Deformed spinal
cord PRV
40
shown are: the volume receiving 95% of the prescription dose (V95%), the dose to 98% of
the volume (D98%) known as the near minimum dose, the D2%, and the dose to 50% of
the volume (D50%) known as the median dose. The superscripts acc and plan correspond
to the accumulated deformed CBCT calculated plans and the original plan respectively.
Target coverage is shown for the tumor bed, or gross tumor volume (GTV) and planning
target volume (PTV). A clinical target volume (CTV) was not drawn for breast boost
treatments as is the clinical practice at this institution.
Patient (
) [%]
(
) [Gy]
(
) [Gy]
(
) [Gy] PTV
1 97.4 (98.6) 9.4 (9.6) 10.6 (10.7) 10.4 (10.3)
2 92.9 (98.8) 10.9 (12.0) 13.3 (13.3) 12.9 (12.9)
3 75.2 (98.8) 7.5 (9.6) 10.5 (10.7) 10.1 (10.3)
4 84.7 (98.1) 7.3 (9.5) 10.8 (10.9) 10.5 (10.6)
GTV
1 99.9 (100.0) 10.2 (9.9) 10.6 (10.7) 10.4 (10.3)
2 100.0 (100.0) 12.7 (12.5) 13.1 (13.1) 12.9 (12.8)
3 100.0 (100.0) 10.2 (10.0) 10.4 (10.4) 10.3 (10.2)
4 96.2 (99.9) 9.2 (10.4) 10.4 (10.8) 10.4 (10.6)
Table 4.2. Dose volume statistics for four breast patients as calculated in the shifted
treatment position. The numbers in brackets correspond to the original plan.
For all patients the target coverage is consistent with the original plan and the GTV is
covered even when the PTV is not. This is expected as the purpose of the PTV is to
create a sufficient planning volume to account for any geometric uncertainties during
treatment [8]. It is unclear if the lack of target coverage for patient 3 is due to a
deformation error as opposed to being representative of the way the patient was treated.
The PTV for this patient was very close to the surface, so a slight change in body contour
could cause a lack of coverage due to insufficient dose buildup in that region. If the
patient was shifted incorrectly, this could also lead to a lack of coverage.
41
Patient (
)
[%]
(
)
[Gy]
(
)
[Gy]
(
)
[Gy] PTV
1 93.0 (98.6) 8.8 (9.6) 10.8 (10.7) 10.5 (10.3)
2 89.2 (98.8) 9.7 (12.0) 13.1 (13.3) 12.8 (12.9)
3 64.5 (98.8) 6.3 (9.6) 10.5 (10.7) 9.9 (10.3)
4 88.1 (98.1) 8.1 (9.5) 10.7 (10.9) 10.4 (10.6)
GTV
1 99.8 (100.0) 10.0 (9.9) 10.7 (10.7) 10.5 (10.3)
2 100.0 (100.0) 12.6 (12.5) 13.0 (13.1) 12.8 (12.8)
3 99.6 (100.0) 9.8 (10.0) 10.4 (10.4) 10.2 (10.2)
4 98.3 (99.9) 9.6 (10.4) 10.6 (10.8) 10.5 (10.6)
Table 4.3. Dose volume statistics for four breast patients as calculated in the un-shifted
pre-treatment position. The numbers in brackets correspond to the original plan.
When the dose is calculated in the initial set-up position, the GTV is still covered while
the PTV is not covered as well as when calculated in the shifted treatment position. The
difference in the V95% for the PTV between the shifted and un-shifted plans is
approximately 4% except for patient 3. This is consistent with the purpose of using a
PTV to account for positioning errors. A study by Harris et al. found that a PTV margin
of 10 mm can be used for radiation therapy treatments to the breast without imaging [9].
The same margin was used for the patients in this study. A study by Ballivy et al.
calculated IMRT plans for eight head and neck cancer patients using margins of 0, 2.5,
and 5 mm. They found significant improvement in target coverage with margins of 2.5
and 5 mm, although there was an increase in dose to critical structures [10].
Figure 4.3 shows the daily dose volume histograms (DVH) for the GTV and PTV for
patient two as calculated in the shifted treatment position (blue curves). Also shown are
the originally planned doses to the targets (red curves) and the dose from the accumulated
plan (pink curves) for the same patient if the original and accumulated plans were scaled
to one fraction. As expected, the GTV is consistently covered while the coverage of the
PTV varies from fraction to fraction. Similarly, Das et al. found that changes in isocenter
location of ±1 cm only changed the dose distribution by ±2% [11]. All shifts for this
study were under 1 cm.
42
Figure 4.3. DVH for patient 2 showing dose the the PTV (top) an GTV (bottom) for each
fraction (blue) compared with the original plan (red) and the accumulated shifted plan
(pink).
The median accumulated dose to the heart, left lung, and right lung are shown in Figure
4.4 for all four patients. The doses to these organs at risk (OAR) do not differ
significantly from the doses calculated in the original plan. In all cases, except the heart
for patient 1, the dose to the OAR was lower or equal when calculated in the shifted
treatment position than the un-shifted position. For the sparing of normal tissues, there is
43
a benefit to shift the patient based on pretreatment imaging. This is increasingly
important when structures are near tolerance levels. In a study by Han et al. of five head
and neck patients, they saw a significant increase to the dose to critical structures when
daily setup corrections were not applied [12].
Figure 4.4. Accumulated D50% for the heart, left lung, and right lung as calculated in the
shifted (red) and un-shifted (green) positions compared with the original plan dose (blue).
In a study of four left-sided breast cancer patients receiving IMRT using helical
tomotherapy, Goddu et al. found that the impact of uncorrected setup errors was small
but not negligible [13]. They saw that shifts of up to 7 mm in the anterolateral direction
and 2.8 mm in the posteromedial direction would not compromise coverage. However,
the dose to the left lung increased remarkably. Qi et al. found that normal breathing had a
clinically insignificant dosimetric impact on 18 breast cancer patients [14]. For prostate
IMRT, treating without shifting can underdose the target but does not cause significant
change in the dose to normal tissue [15,16].
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Heart Lt. Lung Rt. Lung
D5
0%
[G
y]
Patient 1
Original
Shifted
Unshifted
0
0.1
0.2
0.3
0.4
0.5
Heart Lt. Lung Rt. Lung
D5
0%
[G
y]
Patient 2
Original
Shifted
Unshifted
0
0.1
0.2
0.3
Heart Lt. Lung Rt. Lung
D5
0%
[G
y]
Patient 3
Original
Shifted
Unshifted
0
0.1
0.2
0.3
0.4
0.5
0.6
Heart Lt. Lung Rt. Lung
D5
0%
[G
y]
Patient 4
Original
Shifted
Unshifted
44
4.3 Planned Adaptive treatment verification study
The results of the treatment verification study based on the Planned Adaptive calculations
for the CSI and head and neck patients are discussed in this section. The delivered dose
for each fraction of treatment was calculated based on the pretreatment MVCTs and the
fluence sinogram of the day. The resulting dose distributions were then deformed to the
planning CT and compared with the original dose distribution.
4.3.1 Craniospinal irradiation study
The volume of the CTV receiving the prescription dose (VRx) for each fraction of the CSI
treatment is shown in Figure 4.5.
Figure 4.5. Volume of the CTV receiving the prescription dose for each fraction of CSI
treatment. The dashed line represents the planned dose to the CTV for each fraction of
89% for patient 1, 98% for patient 2, and 99% for patients 3 through 5.
When looking at the whole course of treatment (accumulated dose), the VRx for the CTV
was within 1% of the original plan except for patient 2 where it was lower by 5%. The
V95% for the CTV was within 1% of the original plan for all patients. This is shown in
Figure 4.6, which is a plot of the ratio of the dose-volume indices (D2%, D98%, D50%,VRx,
76
78
80
82
84
86
88
90
92
94
96
98
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
VR
x [
%]
Fraction
Patient 1
Patient 2
Patient 3
Patient 4
Patient 5
45
and V95%) of the MVCT calculated dose deformed and accumulated on the planning CT
to that of the planned kVCT calculated dose. The ratios are within 2% of unity for
patients 1, 3, and 4, and within 4% and 5% for patients 5 and 2 respectively.
Figure 4.6. Ratios of CTV dose-volume indices as calculated on the pretreatment MVCT
images to that of the original plan.
The median dose to the CTV for each fraction of treatment is shown in Figure 4.7. The
planned CTV D50% for patient 1 was 1.54 Gy per fraction, and it was 1.83 Gy for the
other patients. On average, patients 1 and 2 received the same CTV D50% as planned,
Patients 3 and 4 received a slightly higher D50% of 1.85 and 1.86 Gy respectively, and
patient 5 received 1.91 Gy, an increase of 4% over the original treatment plan. The target
coverage was consistent with the original treatment plan with the exception of patient 2,
who experienced a 3 kg weight loss over the course of treatment.
0.94
0.96
0.98
1
1.02
1.04
1 2 3 4 5
rati
o (
MV
CT
acc
um
ula
ted
do
se/k
VC
T d
ose
)
Patient
D2%
D98%
D50%
VRx
V95
46
Figure 4.7. The median dose (D50%) to the CTV for each fraction of the CSI
treatment as calculated on each MVCT. The dashed black lines represent
the planned D50%: 1.54 Gy per fraction for patient 1 and 1.83 Gy for patients
2 through 5.
To my knowledge, there are not studies specifically addressing the dosimetric effects of
weight loss on CSI. However, there have been extensive studies on other sites such as
head and neck cancers [12,17-21]. A study by Duma et al. involved 11 head and neck
tomotherapy patients who experienced soft tissue changes of greater than 0.5 cm on each
side. They found an increased dose to the PTV and a larger normal tissue volume
receiving a higher dose than the original plan [17]. Replanning helped to reduce the high
dose to the PTV and improved the dose sparing to the larynx, oral cavity, and spinal cord.
However, there was no significant improvement for the parotid glands. The mean weight
loss at the time of replan was 2.3 kg.
The dose volume statistics for the CTV, heart, kidneys, and lungs are shown in Table 4.4.
The value shown is the ratio of the MVCT calculated dose deformed and accumulated on
the original CT to the dose calculated for the original treatment plan. The average and
standard deviation (SD) are also shown.
1.5
1.55
1.6
1.65
1.7
1.75
1.8
1.85
1.9
1.95
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
D5
0 [
Gy
]
Fraction
Patient 1
Patient 2
Patient 3
Patient 4
Patient 5
47
CTV Heart Right Kidney Left Kidney Right Lung Left Lung
Patient D2 D98 D50 D2 D98 D50 D2 D98 D50 D2 D98 D50 D2 D98 D50 V20 D2 D98 D50 V20
1 1.00 1.01 0.99 0.88 1.14 1.02 0.91 1.23 0.96 1.04 1.08 1.07 1.03 0.97 1.10 1.13 1.05 0.98 1.11 1.19
2 1.00 0.96 1.00 0.92 1.06 1.00 1.02 1.30 1.20 1.05 1.13 1.09 0.97 1.14 1.00 0.83 0.95 1.09 0.93 0.80
3 1.02 1.01 1.01 1.03 1.02 1.02 0.97 1.01 1.03 1.02 1.00 1.02 1.04 0.96 1.03 1.12 1.01 0.96 1.03 1.06
4 1.01 1.02 1.01 0.98 1.13 0.93 0.85 1.11 0.93 0.97 1.10 0.91 1.05 1.03 1.04 1.12 0.96 0.98 1.03 0.89
5 1.04 1.03 1.04 0.88 0.99 0.94 0.99 0.96 1.00 0.97 0.98 0.99 1.03 1.05 1.07 1.21 0.87 0.94 0.93 0.53
Average 1.01 1.01 1.01 0.94 1.07 0.98 0.95 1.12 1.02 1.01 1.06 1.02 1.02 1.03 1.05 1.08 0.97 0.99 1.01 0.89
SD 0.01 0.02 0.02 0.06 0.06 0.04 0.06 0.13 0.09 0.03 0.06 0.06 0.03 0.06 0.03 0.13 0.06 0.05 0.07 0.23
Table 4.4. Ratio of MVCT calculated dose to original plan dose for the five CSI
patients.
The target and critical structures do not receive drastically different doses from that of the
original plan. On average the near maximum, near minimum, and median doses to the
CTV are only 1% above the original planned dose. The ratios for the OAR are shown in
Figure 4.8. The median dose to the heart and kidneys, and the volume of the lungs
receiving 20 Gy (V20Gy) are shown.
Figure 4.8. Ratios of MVCT accumulated to original plan dose-volume indices for the
heart, kidneys and lungs for the five CSI patients.
Although the ratio shows an increase of up to 20% in the doses to the OAR, the absolute
dose difference or change in volume is not clinically significant. The largest increase is
seen in patient 5 for the right lung V20Gy, which increased from 6.3% in the original plan
to 7.6% in the MVCT calculated dose. The absolute dose differences for the heart and
kidney D50% are shown in Table 4.5.
0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1 2 3 4 5
rati
o (
MV
CT
acc
um
ula
ted
do
se/k
VC
T d
ose
Patient
Heart D50%
Rt. Kidney D50%
Lt. Kidney D50%
Rt. Lung V20Gy
Lt. Lung V20Gy
48
Patient 1 2 3 4 5
Heart (Gy) 0.09 -0.01 0.17 -0.6 -0.4
Right Kidney (Gy) -0.26 1.72 0.19 -0.41 -0.03
Left Kidney (Gy) 0.36 0.77 0.09 -0.59 -0.06
Table 4.5. Absolute difference in D50% (MVCT accumulated – Original) for the heart and
kidneys for the CSI patients.
The results from this study indicate that the target coverage and doses to the OAR are not
significantly changed over the course of CSI treatment. The largest documented weight
loss in this study was 4 kg. Kim et al. found that an expansion or contraction of the
external contour of 5 mm in all directions did not significantly change the dose to the
tumor or critical structures [22].
4.3.2 Head and Neck Study
For the head and neck patient, all figures show the dose from the original plan (dashed
line), the daily dose calculated using Planned Adaptive software and deformed to the
original CT (symbols), as well as the accumulated dose on the original planning CT
(dotted line).
Figure 4.9 shows the volume of the three PTVs receiving 95% of the prescription dose
(V95%). The mean ratio of the Planned Adaptive calculated V95% to the original plan was
0.99 ± 0.01 for all PTVs. Figure 4.10 shows the median dose (D50%) to the three PTVs.
We see that the median dose delivered is greater than the planned dose.
49
Figure 4.9. Volume receiving 95% of the prescription dose for all PTVs for each
fraction. Deformed refers to the deformed MVCT calculated dose, original refers
to the original plan dose, and accumulated refers to the deformed accumulated dose.
Figure 4.10. Median dose to the three PTVs for each fraction. Deformed refers
to the deformed MVCT calculated dose, original refers to the original plan
dose, and accumulated refers to the deformed accumulated dose.
94
95
96
97
98
99
100
101
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
V95
% [%
]
Fraction
PTV70deformed
PTV63deformed
PTV56deformed
PTV70original
PTV63original
PTV56original
PTV70accumulated
PTV63accumulated
PTV56accumulated
1.6
1.65
1.7
1.75
1.8
1.85
1.9
1.95
2
2.05
2.1
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
D5
0%
[Gy]
Fraction
PTV70deformed
PTV63deformed
PTV56deformed
PTV70original
PTV63original
PTV56original
PTV70accumulated
PTV63accumulated
PTV56accumulated
50
The mean dose to the parotids is shown in figure 4.11. The dose delivered based on the
Planned Adaptive calculations shows that the parotids are receiving less dose than
originally planned. The average ratio of the Planned Adaptive calculated dose to the
original plan is 0.93 ± 0.05 for the right parotid and 0.87 ± 0.06 for the left.
Figure 4.11. Mean dose to the parotids for each fraction calculated using the
Planned Adaptive software. Deformed refers to the deformed MVCT calculated
dose, original refers to the original plan dose, and accumulated refers to the
deformed accumulated dose.
Figure 4.12 shows the spinal cord and spinal cord PRV D2% for each fraction. The mean
ratios of the Planned Adaptive calculated to the original plan for the spinal cord PRV and
spinal cord are 0.98 ± 0.02 and 1.04 ± 0.02 respectively.
The literature suggests that there can be a decrease in target coverage and an increase in
dose to critical structures due to anatomical changes during treatment [18-20]. The results
of this study agree in terms of target coverage and some critical structures. However, we
saw a decrease in the dose to the parotid glands for this one patient. In this study the
contours were not redrawn on the daily images as they were in other studies. While there
are no studies to my knowledge that have shown a decrease in the dose to the parotids,
there are some that do not show a significant increase [22,23]. Ho et al. recalculated the
0.7
0.75
0.8
0.85
0.9
0.95
1
1.05
1.1
1.15
1.2
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Dm
ean
[G
y]
Fraction
Rt. Parotiddeformed
Lt. Parotiddeformed
Rt. Parotidoriginal
Lt. Parotidoriginal
Rt. Parotidaccumulated
Lt. Parotidaccumulated
51
IMRT treatment plans on pretreatment CBCT for 10 patients with oropharyngeal cancer
and found no significant difference between the planned and delivered maximum dose to
the critical structures, and the mean dose to the contralateral parotid. They found that
although five patients experienced a weight loss ≥10%, there was no remarkable
dosimetric change [23].
Figure 4.12. Near maximum dose per fraction for the spinal cord and spinal cord
PRV. Deformed refers to the deformed MVCT calculated dose, original refers to
the original plan dose, and accumulated refers to the deformed accumulated dose.
4.4 Limitations
One limitation of this study is the assumption that the deformable registration works
perfectly. The deformation works well when deforming images of the same modality and
reasonably well for multi-modality deformations when an electron density to HU
conversion curve is applied to each image. For dose deformation, an inaccurate image
deformation can cause the geometrical position to be skewed and therefore deform the
dose distribution incorrectly. Another limitation is that no heterogeneity corrections were
used in the CBCT breast boost dose calculations. This was done to be consistent with the
original plan. Not taking into account the heterogeneity of the patient limits the accuracy
0.7
0.75
0.8
0.85
0.9
0.95
1
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
D2
% [G
y]
Fraction
Spinal Corddeformed
Spinal Cord PRVdeformed
Spinal Cordoriginal
Spinal Cord PRVoriginal
Spinal Cordaccumulated
Spinal cord PRVaccumulated
52
of the evaluation of dose to critical structures. As with all studies, a larger cohort gives
more validity, so the small patient size of these studies is a limitation of the results.
Ideally the number of patients would be increased for all studies, especially the head and
neck study. Investigating patients with drastic weight loss and noticeable anatomic
changes would allow for a broader picture of the delivered dose to head and neck cancer
patients.
53
References
1. Zhang T, Chi Y, Meldolesi E, et al. Automatic delineation of on-line head-and-neck computed tomography images: Toward on-line adaptive radiotherapy. Int J Radiat Oncol Biol Phys 2007;68:522-530.
2. Nelms BE, Tome WA, Robinson G, et al. Variations in the contouring of organs at risk: Test case from a patient with oropharyngeal cancer. Int J Radiat Oncol Biol Phys 2012;82:368-378.
3. Wang H, Garden AS, Zhang L, et al. Performance evaluation of automatic anatomy segmentation algorithm on repeat or four-dimensional computed tomography images using deformable image registration method. Int J Radiat Oncol Biol Phys 2008;72:210-219.
4. Schaly B, Kempe JA, Bauman GS, et al. Tracking the dose distribution in radiation therapy by accounting for variable anatomy. Phys Med Biol 2004;49:791-805.
5. Pukala J, Staton R ,Langen K. What is the importance of dose recalculation for adaptive radiotherapy dose assessment? AAPM annual meeting. Med Phys. 2012;39:3782.
6. Liu H, Greskovich J, Koyfman S, et al. Evaluation of volumetric change and dosimetric discrepancy with daily cone-beam ct for patients with head-and-neck cancer. AAPM annual meeting. Med Phys. 2012;39:3782.
7. Su F, Chen Z ,Nath R. A dosimetric assessment of rectum and bladder using deformable registration in image-guided adaptve prostate imrt. AAPM annual meeting. Med Phys. 2011;38:3448.
8. Icru report 50; prescribing, recording, and reporting photon beam therapy, 1993. 9. Harris EJ, Donovan EM, Coles CE, et al. How does imaging frequency and soft tissue
motion affect the ptv margin size in partial breast and boost radiotherapy? Radiother Oncol 2012;103:166-171.
10. Ballivy O, Parker W, Vuong T, et al. Impact of geometric uncertainties on dose distribution during intensity modulated radiotherapy of head-and-neck cancer: The need for a planning target volume and a planning organ-at-risk volume. Curr Oncol 2006;13:108-115.
11. Das IJ, Cheng CW, Fosmire H, et al. Tolerances in setup and dosimetric errors in the radiation treatment of breast cancer. Int J Radiat Oncol Biol Phys 1993;26:883-890.
12. Han C, Chen YJ, Liu A, et al. Actual dose variation of parotid glands and spinal cord for nasopharyngeal cancer patients during radiotherapy. Int J Radiat Oncol Biol Phys 2008;70:1256-1262.
13. Goddu SM, Yaddanapudi S, Pechenaya OL, et al. Dosimetric consequences of uncorrected setup errors in helical tomotherapy treatments of breast-cancer patients. Radiother Oncol 2009;93:64-70.
14. Qi XS, White J, Rabinovitch R, et al. Respiratory organ motion and dosimetric impact on breast and nodal irradiation. Int J Radiat Oncol Biol Phys 2010;78:609-617.
15. Algan O, Jamgade A, Ali I, et al. The dosimetric impact of daily setup error on target volumes and surrounding normal tissue in the treatment of prostate cancer with intensity-modulated radiation therapy. Med Dosim 2012;37:406-411.
54
16. Orton NP ,Tome WA. The impact of daily shifts on prostate imrt dose distributions. Med Phys 2004;31:2845-2848.
17. Duma MN, Kampfer S, Schuster T, et al. Adaptive radiotherapy for soft tissue changes during helical tomotherapy for head and neck cancer. Strahlenther Onkol 2012;188:243-247.
18. Hansen EK, Bucci MK, Quivey JM, et al. Repeat ct imaging and replanning during the course of imrt for head-and-neck cancer. Int J Radiat Oncol Biol Phys 2006;64:355-362.
19. Lee C, Langen KM, Lu W, et al. Assessment of parotid gland dose changes during head and neck cancer radiotherapy using daily megavoltage computed tomography and deformable image registration. Int J Radiat Oncol Biol Phys 2008;71:1563-1571.
20. O'Daniel JC, Garden AS, Schwartz DL, et al. Parotid gland dose in intensity-modulated radiotherapy for head and neck cancer: Is what you plan what you get? Int J Radiat Oncol Biol Phys 2007;69:1290-1296.
21. Robar JL, Day A, Clancey J, et al. Spatial and dosimetric variability of organs at risk in head-and-neck intensity-modulated radiotherapy. Int J Radiat Oncol Biol Phys 2007;68:1121-1130.
22. Kim S, Liu CR, Palta JR, et al. Dose perturbation due to contour change in head and neck imrt. ASTRO annual meeting. 2003;57:S408-S408.
23. Ho KF, Marchant T, Moore C, et al. Monitoring dosimetric impact of weight loss with kilovoltage (kv) cone beam ct (cbct) during parotid-sparing imrt and concurrent chemotherapy. Int J Radiat Oncol Biol Phys 2012;82:e375-382.
55
Chapter Five
Conclusions
Contents
5.1 Dose verification studies….. 55
5.2 Future work………………. 56
The use of daily image guidance in radiation therapy is becoming common practice as
treatment techniques become more conformal and have higher dose gradients. While
these techniques allow for more sparing of normal tissues, accurate delivery is a necessity
to realize the benefits.
In addition to ensuring proper position prior to delivery, daily imaging allows us to
monitor anatomical changes and to assess the need for adaptive planning. Ideally,
adaptive radiotherapy would be possible on the fly. There are an increasing number of
tools available to assess the current treatment on the daily image to determine the need
for a possible replan. Tools also exist to assist in the automatic segmentation of contours
and perform dose calculations on the daily image. One of the limiting factors in on-the-
fly adaptive radiotherapy is the time required for deformable registration, contour
propagation, contour modification by a physician, recalculation of dose distribution, and
if needed plan re-optimization. The tools are available, yet are not currently fast or
automatic enough to be a reality with the patient waiting on the treatment couch.
5.1 Dose verification studies
In this work, several adaptive tools were utilized. Daily cone beam and megavoltage CT
images were registered with the planning CT using deformable techniques in MIM
Software. The transformation map was also applied to the contours and the dose
distribution. The daily MVCT was rigidly registered with the planning CT using the
Planned Adaptive module, which calculated the delivered dose distribution.
Calculation of breast boost VMAT treatments using two isocenter positions on daily
CBCT images showed that the PTV margins used in this clinic are adequate to account
56
for position variations. The GTV was covered as planned with either isocenter location.
Critical structures received slightly higher doses if the plan was calculated without
applying shifts but not by a significant amount.
Calculation of helical tomotherapy IMRT plans on CSI and head and neck patients based
on daily MVCT imaging showed a slight decrease in target coverage yet no significant
increase in the dose to critical structures.
5.2 Future work
A larger patient set would give a more complete picture of the effects of changing
anatomy on dose delivery and more data for the evaluation of margins used. For the
breast boost study, different margin expansions could be evaluated to determine a smaller
margin that would still ensure proper coverage while sparing more normal tissue. The
inclusion of patients in the Planned Adaptive study that received replans during treatment
would allow for an investigation of the effects of replanning on dose distribution.
Calculating the original plan on the replanning CT as well as the daily MVCT for the
whole course of treatment would give a dosimetric comparison of replanning versus not
replanning. Reoptimized treatment plans could be generated on the daily images and then
accumulated using MIM software to investigate an optimal replanning frequency.
57
Abbreviations
3D Three dimension
3D-CRT Three dimension conformal radiation therapy
AAA Analytic anisotropic algorithm
ART Adaptive radiation therapy
CBCT Cone beam computed tomography
CC Cross correlation
CSI Craniospinal irradiation
CT Computed tomography
CTV Clinical target volume
DVH Dose volume histogram
EBRT External beam radiation therapy
FFD Free-form deformation
GTV Gross tumor volume
HT Helical tomotherapy
HU Hounsfield Unit
ICRU International Commission on Radiation Units and Measurements
IGART Image-guided adaptive radiation therapy
IGRT Image-guided radiation therapy
IMRT Intensity modulated radiation therapy
IVDT Image value to density table
kV Kilovoltage
MI Mutual information
MLC Multileaf collimator
MRI Magnetic resonance imaging
MV Megavoltage
OAR Organ at risk
OBI On-board imaging
PET Positron emission tomography
PRV Planning organ at risk volume
PTV Planning target volume
ROI Region of interest
SBRT Stereotactic body radiosurgery
SRS Stereotactic radiosurgery
SSD Sum of squared differences
TPS Treatment planning system
VMAT Volumetric modulated arc therapy
58
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16. O'Daniel JC, Garden AS, Schwartz DL, et al. Parotid gland dose in intensity-modulated radiotherapy for head and neck cancer: Is what you plan what you get? Int J Radiat Oncol Biol Phys 2007;69:1290-1296.
17. Robar JL, Day A, Clancey J, et al. Spatial and dosimetric variability of organs at risk in head-and-neck intensity-modulated radiotherapy. Int J Radiat Oncol Biol Phys 2007;68:1121-1130.
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50. Wang H, Garden AS, Zhang L, et al. Performance evaluation of automatic anatomy segmentation algorithm on repeat or four-dimensional computed tomography images using deformable image registration method. Int J Radiat Oncol Biol Phys 2008;72:210-219.
51. Schaly B, Kempe JA, Bauman GS, et al. Tracking the dose distribution in radiation therapy by accounting for variable anatomy. Phys Med Biol 2004;49:791-805.
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54. Su F, Chen Z ,Nath R. A dosimetric assessment of rectum and bladder using deformable registration in image-guided adaptve prostate imrt. AAPM annual meeting. Medical Physics. 2011;38:3448.
55. Harris EJ, Donovan EM, Coles CE, et al. How does imaging frequency and soft tissue motion affect the ptv margin size in partial breast and boost radiotherapy? Radiother Oncol 2012;103:166-171.
56. Ballivy O, Parker W, Vuong T, et al. Impact of geometric uncertainties on dose distribution during intensity modulated radiotherapy of head-and-neck cancer: The need for a planning target volume and a planning organ-at-risk volume. Curr Oncol 2006;13:108-115.
57. Das IJ, Cheng CW, Fosmire H, et al. Tolerances in setup and dosimetric errors in the radiation treatment of breast cancer. Int J Radiat Oncol Biol Phys 1993;26:883-890.
61
58. Goddu SM, Yaddanapudi S, Pechenaya OL, et al. Dosimetric consequences of uncorrected setup errors in helical tomotherapy treatments of breast-cancer patients. Radiother Oncol 2009;93:64-70.
59. Qi XS, White J, Rabinovitch R, et al. Respiratory organ motion and dosimetric impact on breast and nodal irradiation. Int J Radiat Oncol Biol Phys 2010;78:609-617.
60. Algan O, Jamgade A, Ali I, et al. The dosimetric impact of daily setup error on target volumes and surrounding normal tissue in the treatment of prostate cancer with intensity-modulated radiation therapy. Med Dosim 2012;37:406-411.
61. Orton NP ,Tome WA. The impact of daily shifts on prostate imrt dose distributions. Med Phys 2004;31:2845-2848.
62. Duma MN, Kampfer S, Schuster T, et al. Adaptive radiotherapy for soft tissue changes during helical tomotherapy for head and neck cancer. Strahlenther Onkol 2012;188:243-247.
63. Kim S, Liu CR, Palta JR, et al. Dose perturbation due to contour change in head and neck imrt. ASTRO annual meeting. 2003;57:S408-S408.
64. Ho KF, Marchant T, Moore C, et al. Monitoring dosimetric impact of weight loss with kilovoltage (kv) cone beam ct (cbct) during parotid-sparing imrt and concurrent chemotherapy. Int J Radiat Oncol Biol Phys 2012;82:e375-382.