purely data-driven respiratory motion compensation...
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Matthew J. Riblett – Medical Physics Virginia Commonwealth University
Modified General Template 2015 ANNUAL MAC-AAPM CONFERENCE:
Purely Data-Driven Respiratory Motion Compensation Methods for 4D-CBCT Image
Registration and Reconstruction
M J Riblett1, E Weiss1, G E Christensen2, and G D Hugo1 1 Virginia Commonwealth University, 2 University of Iowa
Baltimore, MD | October 2nd 2015
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
Modified General Template
SUPPORT AND DISCLOSURES This work was supported by the National Cancer Institute of the National Institutes of Health under award number R01-CA-166119. The authors have no potential conflicts of interest to disclose for this study.
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Rationale for Motion Compensation
Streaking (View Aliasing) With Projection Binning (4D-CBCT)
Motion Blurring Without Projection Binning (3D-CBCT)
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Motion Compensation Methods
1. Reconstruct 4D-CBCT frames from a subset of the projection dataset binned according to a signal (i.e. respiration).
2. Compute an estimate of motion in each reconstructed frame and deform image.
1. Motion model is known upfront or computed prior to 4D-CBCT image reconstruction.
2. Full projection dataset is deformed based on motion model during reconstruction of each frame.
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Backproject-Deform Example: Li, 2006
Deform-Backproject Example: Rit, 2009
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Motion Compensation Methods
Backproject-Deform Example: Li, 2006
Advantages:
+ Motion model can be created directly from 4D-CBCT dataset.
+ Day of treatment modeling.
Disadvantages:
- Projection binning results in view aliasing artifact.
- Registration (motion modeling) is challenging due to poor image quality.
Deform-Backproject Example: Rit, 2009
Advantages:
+ Uses full projection dataset for every frame reconstruction.
+ View aliasing artifact is reduced.
Disadvantages:
- Requires an a priori motion model prior to reconstruction.
- May fail to accommodate large variations in patient anatomy or motion over the course of treatment.
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Purpose of Research To develop purely data-driven 4D-CBCT workflows combining both motion compensation methods to enhance image quality.
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2. Deform-Backproject Projection-Warped Reconstruction
1. Backproject-Deform Registration of 4D-CBCT
Improved CBCT Image
Motion Model
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Study Contributions
Combination of Both Motion Compensation Methods • Backproject-Deform:
Build motion model (DVF) from groupwise registration of respiratory phase-correlated 4D-CBCT reconstruction.
• Deform-Backproject:
Apply motion model to warp full projection data during subsequent motion-compensated 4D-CBCT reconstruction.
Application of Groupwise Registration to 4D-CBCT • Similar methods have demonstrated registration advantages
for fanbeam CT, MR, and US.
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Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Study Contributions
Purely Data-Driven Methods • Data-driven methods offer solutions robust to variations in
patient anatomy and motion over the course of treatment.
• A priori motion modeling may be unable to handle large differences in patient anatomy or motion during treatment.
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Week 2 4D-CT Week 7 4D-CT
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Groupwise Registration
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Registration to the target frame occurs simultaneously for all source frames mitigating frame-to-frame bias in the resulting 4D transform.
IT
IS2
IS1
IS3
IS4 T1
T2 T3
T4
~
Groupwise Registration
IT
IS2
IS1
IS3
IS4 TG,1
TG,2 TG,3
TG,4
~
Conventional Registration
Registrations between source and target frames occur independently, permitting frame-to-frame bias to manifest in the 4D transform.
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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③ Registration with Reconstruction of Preselected Frame ④ Registration with Reconstruction of Mean Frame
① Registration to Preselected Frame ② Registration to Mean Frame
• Workflows can be subdivided by inclusion of one or both motion-compensation methods:
Developed Workflows
Registration Only
Registration with Projection-Warping Reconstruction
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Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Data Sources and Implementation
Eight Clinical Patient Datasets
• Long CBCT acquisitions (single rotation)
• 2200-3500 projections per patient set.
Respiratory Signal Extraction
• Amsterdam shroud as implemented in RTK*. (Zijp, 2004)
• Used for projection sorting and reconstruction.
Registration
• Elastix Toolkit 4.7 (Klein, 2010; Shamonin, 2014)
• Insight Toolkit (ITK) 4.7.0 (Yoo, 2002)
Reconstruction
• RTK 1.0 (Rit, 2014; openRTK.org)
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Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Qualitative Results
Free-Breathing Mean Registered to Mean Frame Registered to Mean Frame
and MC-Reconstructed
Free-Breathing 4D-CBCT Registered to Preselected Frame Registered to Preselected Frame
and MC-Reconstructed
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Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Qualitative Results
Free-Breathing 4D-CBCT Registered to Preselected Frame Registered to Preselected Frame
and MC-Reconstructed
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Free-Breathing Mean Registered to Mean Frame Registered to Mean Frame
and MC-Reconstructed
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Qualitative Results
Free-Breathing 4D-CBCT Registered to Preselected Frame Registered to Preselected Frame
and MC-Reconstructed
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Free-Breathing Mean Registered to Mean Frame Registered to Mean Frame
and MC-Reconstructed
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Quantitative Results
Statistical noise reduction relative to 4D-CBCT
Mean Air Aorta Soft Tissue
Reg. Only 63%
(σ=12%) 51%
(σ=20%) 34%
(σ=11%)
Reg./Recon. 68%
(σ=15%) 55%
(σ=22%) 36%
(σ=13%)
Preselected Air Aorta Soft Tissue
Reg. Only 62%
(σ=16%) 50%
(σ=24%) 32%
(σ=13%)
Reg./Recon. 67%
(σ=15%) 43%
(σ=21%) 36%
(σ=13%)
Air
Aorta
Initial CBCT Air Aorta Soft Tissue
Free-Breathing Mean
64% (σ=13%)
54% (σ=20%)
41% (σ=16%)
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Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Quantitative Results
Increase in edge sharpness (TIS) relative to 4D-CBCT
Mean Target Frame TIS Increase*
Reg. Only 75% (σ=98%)
Reg./Recon. 52% (σ=54%)
Preselected Target Frame TIS Increase*
Reg. Only 65% (σ=51%)
Reg./Recon. 49% (σ=35%)
Initial CBCT TIS Increase
Free-Breathing Mean -3% (σ=56%)
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-10
00
-50
00
45 55 65 75 85
No
rmalized
CBC
T In
tensit
ies
Z-axis Coordinate [mm]
Diaphragm Dome Profile
Mean Initial
4D-CBCT
Mean Frame,
Reg+Recon
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Existing Challenges
Respiratory Signal
• Signal acquired from either RPM or Amsterdam shroud for projection sorting and/or reconstruction.
• Choice of parameters for Amsterdam shroud impact ability to extract signal.
Noise and Artifacts
• Deleterious image elements cause errors in registration: latches on to erroneous signal and guides transform.
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Projection-warped reconstruction using: A. accurate signal B. erroneous signal
A
B
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Conclusions
Mean v. Preselected Frame • Image quality improvement is
similar for both methods.
• Current implementation offers computational advantage with preselected.
Reconstruction Advantage • Registration improves edge
sharpness and noise.
• MC reconstruction improves edge sharpness and image noise while also mitigating appearance of some artifacts.
Free-Breathing 4D-CBCT
Registration + Reconstruction
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Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Conclusions
Respiratory Signal Critical
• Correct acquisition and interpretation of respiratory signal greatly impacts initial and motion-compensated reconstruction.
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Projection-Warped Reconstructions
A
B
0
0.5
1
0 200 400 600
0
0.5
1
0 200 400 600
Data-driven Respiratory Signals
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Future Directions
Improve Signal Acquisition • Shroud generation, signal
extraction, projection sorting, etc.
Additional Iterations • Currently single pass
• Multiple iterations may continue to improve.
Refine Workflow Parameters • B-spline grid spacing
reduction, iterations, etc.
Additional Patients • Near-term: 10-20 patients
64mm B-Spline
Grid
16mm B-Spline
Grid
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Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Highlighted References
• Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW. elastix: a toolbox for intensity based medical image registration. IEEE Transactions on Medical Imaging, 29 (1): 196–205; January 2010.
• Metz CT, Klein S, Schaap M, van Walsum T, Niessen WJ. Nonrigid registration of dynamic medical imaging data using nd + t b-splines and a groupwise optimization approach. Medical Image Analysis, 15 (2): 238–49, April 2011.
• Li T, Schreibmann E, Yang Y, Xing L. Motion correction for improved target localization with on-board cone-beam computed tomography. Physics in Medicine and Biology, 51(2): 253, 2006
• Rit S, Wolthaus JW, van Herk M, Sonke JJ. On-the-fly motion-compensated cone-beam CT using an a priori model of the respiratory motion. Medical Physics, 36 (6): 2283-96; June 2009.
• Shamonin DP, Bron EE, Lelieveldt BPF, Smits M, Klein S, Staring M. Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer’s disease. Frontiers in Neuroinformatics, 7 (50): 1-15; January 2014.
• Yoo TS, Ackerman MJ, Lorensen WE, Schroeder W, Chalana V, Aylward S, Metaxas D, Whitaker R. Engineering and algorithm design for an image processing API: a technical report on ITK – the insight toolkit. Proc. of Medicine Meets Virtual Reality, Westwood J, ed., IOS Press Amsterdam: 586-592; 2002.
• Zijp L, Sonke JJ, van Herk M. Extraction of the respiratory signal from sequential thorax cone-beam X-ray images. International Conference on the Use of Computers in Radiation Therapy (ICCR). Seoul, Republic of Korea: Jeong Publishing: 507-509; 2004.
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Special Thanks
Dr. Geoffrey Hugo
Advisor
Dr. Gary Christensen
Collaborator
Nicky Mahon
Labmate
Eric Laugeman
Labmate
Dr. Elisabeth Weiss
Collaborator
Chris Guy
Collaborator
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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ANY COMMENTS, QUESTIONS, OR SUGGESTIONS?
Thanks for listening.
Matthew J. Riblett: [email protected]
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
Modified General Template
PRACTICAL EXAMPLES OF CBCT COMPLICATIONS
Appendix I:
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Factors Affecting 4D CBCT Quality
• Undersampling and Reconstruction Artifacts Cupping and Streaking
View aliasing caused by frame-binning of projections and inherent undersampling in each frame.*
• Motion Related Degradation Averaging motion in 3D results in blurred boundaries and
structures.
• Variable Gantry Motion and Flexing Variable image centroid
Image blurring
• Increased X-ray Scattering Over CT ( SPR) Decreased voxel noise in individual projections
Decreased contrast (CNR)
Incorrect CT numbers (~30% error in MV CBCT)
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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MO
TIO
N E
FFEC
TS
Blurring of Masses
Blurring of Vessels and
Tissue
Blurring of Diaphragm
Image: Delmon et al. (2011)
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UN
DER
SA
MPLIN
G
Streaking (View Aliasing)
Axial Sagittal
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Practical Examples
Example CT acquisition Projection Undersampling
In CBCT
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Practical Examples
Example CT acquisition Motion-Averaged Blurring in CBCT
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Other Examples…
Cupping Streaking Variable gantry trajectory and CBCT flexing
Self-attenuation at center and scatter out of plane
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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EXISTING MOTION COMPENSATION METHODS
Appendix II:
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Sampling of Existing Motion Compensation Methods
Authors Method Findings Limits
Rit et al. (2009)
A priori motion modeling with projection warping reconstruction
• Develops motion model from respiratory signal.
• DVF compensates for motion in 4D CBCT reconstruction.
• Requires a planning CT and an a priori motion model.
Delmon et al. (2011)
Sliding lung mask registration with mutual information metric
• Masks limit registration to ‘sliding’ lung anatomy.
• Registration of frames results in DVF for projection warping during reconstruction.
• Requires masking of the lung anatomy which may require manual intervention.
Metz et al. (2011)
Groupwise-cyclic registration with temporal variance metric
• Implementation can register multiple temporal frames to reference and ‘average’ frames.
• Has been applied to CT, MR, and US imaging.
• Not yet applied to CBCT.
• Images are transformed; not projection warping.
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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A priori Motion Model Method
• Rit et al. (2009) – Acquires a 4D planning
CT with respiratory signal.
– Offline model correlates signal to organ motion: forms 4D DVF.
– CBCT projections are acquired and respiratory signal extracted.
– 3D CBCT image is reconstructed using 4D DVF to warp projections.
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Sliding Mask Method
• Delmon et al. (2011) – Applies mutual
information metric with series of ‘sliding’ masks
– DVF is applied during CBCT reconstruction to correct projections.
– Results in an image with sharper vessels and tumor boundaries.
…Requires masks.
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Groupwise Cyclic Method
• Metz et al. (2011): – Computes cost metric as
variance in the temporal dimension.
– Registers to an ‘average’ phase instead of a reference phase.
– Imposes smooth cyclic motion constraint.
– Applied to CT, MR and US imaging.
…not to CBCT.
Input CT Image
Registered CT Image
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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PROPOSED WORKFLOW DIAGRAMS
Appendix III:
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Registration Method Registration-Only Reg. and Reconstruction
Mean Target Frame Mean image of
groupwise registered frame Reconstructed image at
mean target frame
Preselected Target Frame Mean image of
groupwise registered frame Reconstructed image at preselected target frame
Developed Workflows
• Reconstruction(s) of target frame(s)
• Registration(s) to target frame(s)
• DVF generation
• Initial 4D image • Workflow
parameters
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Mean Frame Registration with(out) Reconstruction Methods
• Goal Render an improved image of the patient at the 3D ‘mean’ frame of the respiratory cycle.
• Method Implement the groupwise registration with elastix VarianceOverLastDimension metric (VOLDM), and the reconstruction with RTK.
• Considerations Registers to automatically defined ‘average’ temporal frame with no respiratory cycle weighting.
Initial Average Frame FDK
Motion Compensated
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Preselected Frame Registration with(out) Reconstruction Methods
• Goal Render an improved image of the patient at each of the original frames of 4D image.
• Method Implement a series of groupwise ‘4D’ registrations with elastix mean squared differences (MSD) metric, and the reconstruction with RTK
• Considerations Registers original image to a set of pseudo-4D frames:
10 frames = 10 registrations. Initial Frame 0
FDK Motion
Compensated
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Hierarchical 4D Registration to 3D Frame -
- Registration to Mean Frame -
Initial 4D Image
Initial Registration Parameters
Hierarchical Registration VOLDM and TBEP:
Elastix and Transformix
4D Transform to
‘Average’ Phase Image
Accept Result
Return Image and Transform
Registration with Adjusted Metric Parameters:
Elastix & Transformix
Acceptance Criteria
No
Adjust Registration Parameters
A priori Parameters and Metrics
Yes
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Registration with 3D MC Reconstruction -
- Reconstruction of Mean Frame -
3D DVFs to Phase [0…N]
Initial 4D Image
Initial Registration Parameters
Projection Data
Phase Signal
Hierarchical Registration VOLDM and TBEP:
Elastix and Transformix
3D Motion Compensated Reconstructions
RTK or Simple RTK
3D DVFs to Phase [0…N]
Accept Image
Return Image
Registration with Adjusted Metric
Parameters: Elastix & Transformix
Acceptance Criteria
Adjust Registration Parameters
A priori Parameters and Metrics
Yes No
4D DVFs to Phase [0…N]
3D Average Frame Recon.
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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3D DVFs to Phase [0…N]
Initial 4D Image
Initial Registration Parameters
Projection Data
Phase Signal
Hierarchical Registration MSD and TBEP:
Elastix and Transformix
3D DVFs to Phase [0…N]
Accept Image
Return Image
Registration with Adjusted Metric
Parameters: Elastix & Transformix
Acceptance Criteria
Adjust Registration Parameters
A priori Parameters and Metrics
Yes No
4D Stacking of Phase Images ribPy or Matlab
Stacked 4D-MC Image
4D DVFs to Phase [0…N]
Reconst. 3D Phase
Images
Registration with 4D MC Reconstruction -
- Reconstruction of 3D Frames [0,N] and 4D Stacking -
Hierarchical Registrations MSD and TBEP:
Elastix and Transformix
3D Motion Compensated Reconstructions
RTK or Simple RTK
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Development Steps
Implementation
1. VOLDM methods based on the work of Metz et al., and MSD methods with Python backend.
2. Tested registration settings with clinical images parametrically.
3. Improved the methods’ performance with phantom model studies.
4. Reconstruct images with motion compensation: projection warping according to DVF
Deliverable Component
1. Python framework (ribPy) for image generation, manipulation, basic masking, and sampling.
2. Parametric study tool for automatic review of registrations.
3. Geometric phantom generator for thorax modeling and known deformations.
4. Added HNC file I/O and flood field correction to in-house RTK deployment.
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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Observed Challenges
• Driving Data Quality of initial and motion-compensated images are subject to quality of acquired data (respiratory signal, projections, flood field, etc.)
• Static Anatomy Close proximity of static and mobile anatomy introduces challenges in registration.
• Computational Cost Registration and additional reconstruction carry non-trivial computational expense.
64mm B-Spline
Grid
16mm B-Spline
Grid
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
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PHANTOM MODELS Appendix IV:
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Simple Phantom Model
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Simple Phantom Model
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Geometric Anatomical Phantom
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RESPIRATORY SIGNAL EXTRACTION
Appendix V:
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Respiratory Signal Extraction
Projection-Warped Reconstructions (Motion-compensated per DVF)
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Data-driven Respiratory Signals (Amsterdam shroud-type signal)