purely data-driven respiratory motion compensation...

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


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



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



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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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



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.



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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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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http://www.openrtk.org



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


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Qualitative Results



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Qualitative Results



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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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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



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



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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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



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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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.



Special Thanks

Dr. Geoffrey Hugo

Advisor

Dr. Gary Christensen

Collaborator

Nicky Mahon

Labmate

Eric Laugeman

Labmate

Dr. Elisabeth Weiss

Collaborator

Chris Guy

Collaborator



ANY COMMENTS, QUESTIONS, OR SUGGESTIONS?

Thanks for listening.

Matthew J. Riblett: [email protected]



PRACTICAL EXAMPLES OF CBCT COMPLICATIONS

Appendix I:



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)



MO

TIO

N E

FFEC

TS

Blurring of Masses

Blurring of Vessels and

Tissue

Blurring of Diaphragm

Image: Delmon et al. (2011)



UN

DER

SA

MPLIN

G

Streaking (View Aliasing)

Axial Sagittal



Practical Examples

Example CT acquisition Projection Undersampling

In CBCT



Practical Examples

Example CT acquisition Motion-Averaged Blurring in CBCT



Other Examples…

Cupping Streaking Variable gantry trajectory and CBCT flexing

Self-attenuation at center and scatter out of plane



EXISTING MOTION COMPENSATION METHODS

Appendix II:



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.



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.



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.



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



PROPOSED WORKFLOW DIAGRAMS

Appendix III:



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



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



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



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



Registration with 3D MC Reconstruction -

- Reconstruction of Mean Frame -

3D DVFs to Phase [0…N]

Initial 4D Image


Projection Data

Phase Signal

Hierarchical Registration VOLDM and TBEP:


3D Motion Compensated Reconstructions

RTK or Simple RTK


Accept Image

Return Image

Registration with Adjusted Metric

Parameters: Elastix & Transformix

Acceptance Criteria



Yes No


3D Average Frame Recon.




Initial 4D Image


Projection Data

Phase Signal

Hierarchical Registration MSD and TBEP:



Accept Image

Return Image

Registration with Adjusted Metric

Parameters: Elastix & Transformix

Acceptance Criteria



Yes No

4D Stacking of Phase Images ribPy or Matlab

Stacked 4D-MC Image


Reconst. 3D Phase

Images

Registration with 4D MC Reconstruction -

- Reconstruction of 3D Frames [0,N] and 4D Stacking -

Hierarchical Registrations MSD and TBEP:


3D Motion Compensated Reconstructions

RTK or Simple RTK



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.



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



PHANTOM MODELS Appendix IV:



Simple Phantom Model



Geometric Anatomical Phantom



RESPIRATORY SIGNAL EXTRACTION

Appendix V:



Respiratory Signal Extraction

Projection-Warped Reconstructions (Motion-compensated per DVF)

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Data-driven Respiratory Signals (Amsterdam shroud-type signal)

purely data-driven respiratory motion compensation...

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