Markerless Tracking-Based Registration for Medical Image Motion Correction
This addresses motion correction in medical imaging for swallowing analysis, offering a more precise tool for clinicians, though it is incremental as it builds on existing tracking and registration techniques.
The study tackled the problem of isolating swallowing dynamics from interfering patient motion in videofluoroscopy by developing a markerless tracking-based registration pipeline, which outperformed leading methods like ANTs, LDDMM, and VoxelMorph in metrics such as MSE and SSIM.
Our study focuses on isolating swallowing dynamics from interfering patient motion in videofluoroscopy, an X-ray technique that records patients swallowing a radiopaque bolus. These recordings capture multiple motion sources, including head movement, anatomical displacements, and bolus transit. To enable precise analysis of swallowing physiology, we aim to eliminate distracting motion, particularly head movement, while preserving essential swallowing-related dynamics. Optical flow methods fail due to artifacts like flickering and instability, making them unreliable for distinguishing different motion groups. We evaluated markerless tracking approaches (CoTracker, PIPs++, TAP-Net) and quantified tracking accuracy in key medical regions of interest. Our findings show that even sparse tracking points generate morphing displacement fields that outperform leading registration methods such as ANTs, LDDMM, and VoxelMorph. To compare all approaches, we assessed performance using MSE and SSIM metrics post-registration. We introduce a novel motion correction pipeline that effectively removes disruptive motion while preserving swallowing dynamics and surpassing competitive registration techniques.