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MR2CBCT: Restoring and Extending Automated CBCT-MRI Registration for TMJ Analysis

Key Investigators

• Eduardo Duarte Caleme (University of North Carolina, USA)
• Lucia Cevidanes (University of North Carolina, USA)
• Paul Dumont (University of North Carolina, USA)
• Alex Buisson (University of North Carolina, USA)
• Steve Pieper (Isomics, USA)
• Gaelle Leroux (CPE Lyon, France)
• Juan Prieto (University of North Carolina, USA)
• Alban Gaydamour (CPE Lyon, France)

Project Description

MRI-to-CBCT registration remains a pressing challenge in medical imaging. Simultaneous visualization of hard and soft tissue structures benefits both clinicians and patients in the diagnosis of temporomandibular degenerative joint disease. Challenges remain in accurately registering these two modalities due to differences in intensity distributions that complicate mutual information optimization, as well as the necessity for initial manual alignment, which can prove unintuitive and challenging for clinicians using current 3D Slicer tools.

Objective

1. Fix issues with the current registration pipeline that impede it from producing appropriate outputs.
2. Implement clinician-friendly manual registration tools.
3. Expand options in both fully automated and clinician-in-the-loop workflows for multi modal registration between MRI and CBCT.

Approach and Plan

1. Present the restored MR2CBCT pipeline and validated results to the community.
2. Explore improved Elastix parameter strategies to increase the capture range beyond the current limits.
3. Present, test, and optimize the new clinician-friendly manual registration tools.
4. Discuss validation framework design.

Progress and Next Steps

1. Fixed a critical bug in AREG_MRI.py where process_images() was never called.
2. Fixed naming convention restrictions throughout the pipeline that proved unintuitive for clinicians.
3. Fixed a mask size mismatch crash in apply_mask.py and a left/right label swap in TMJ crop side detection.
4. Implemented clinician-friendly manual approximation tools, featuring options to freely translate the volume by dragging in slice views, rotate by dragging the circle edge, set a custom center of rotation, and center the MRI at the CBCT center of mass for extremely distant cases. This makes registration significantly easier and more accessible to clinicians and students.
5. Successfully ran the registration pipeline on 25 cases outside of the initial validation sample, yielding clinically satisfying results.
6. Next Steps: Robust automated approximation, increased Elastix capture range, and quantitative validation utilizing Target Registration Error (TRE).

Illustrations

No response

Background and References

Initial images

Center MRI on CBCT function

Clinician manual registration

Elastix Refinement