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 Riblett 1 , E Weiss 1 , G E Christensen 2 , and G D Hugo 1 1 Virginia Commonwealth University, 2 University of Iowa Baltimore, MD | October 2 nd 2015
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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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Matthew J. Riblett – Medical Physics Virginia Commonwealth University
Modified General Template
Rationale for Motion Compensation
Streaking (View Aliasing) With Projection Binning (4D-CBCT)
Motion Blurring Without Projection Binning (3D-CBCT)
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Matthew J. Riblett – Medical Physics Virginia Commonwealth University
Modified General Template
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
Modified General Template
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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Matthew J. Riblett – Medical Physics Virginia Commonwealth University
Modified General Template
Purpose of Research To develop purely data-driven 4D-CBCT workflows combining both motion compensation methods to enhance image quality.
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
Modified General Template
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
Modified General Template
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
• 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
Matthew J. Riblett – Medical Physics Virginia Commonwealth University
Modified General Template
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
Modified General Template
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
Modified General Template
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
Modified General Template
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
Modified General Template
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.