无需标记点追踪，精准分离吞咽运动与患者头动干扰
Markerless Tracking-Based Registration for Medical Image Motion Correction
- 用无标记追踪技术识别并分离吞咽过程中的不同运动源
- 稀疏追踪点生成的形变场优于ANTs、LDDMM等主流配准方法
- 适合需要高精度运动校正的临床视频荧光成像分析场景
本研究聚焦于在视频荧光成像（videofluoroscopy）中分离吞咽动力学与干扰性患者运动，该技术通过X射线记录患者吞咽含造影剂的食团。录制过程包含头动、解剖结构位移和食团传输等多种运动源。为实现吞咽生理的精确分析，需消除干扰性运动，特别是头动，同时保留关键吞咽动态。传统光流方法因闪烁和不稳定性导致结果不可靠，难以区分不同运动组分。我们评估了多种无标记追踪方法（CoTracker、PIPs++、TAP-Net），量化其在关键医学兴趣区域的追踪精度。结果显示，即使使用稀疏追踪点生成的形变场，也优于当前主流注册方法如ANTs、LDDMM和VoxelMorph。通过均方误差（MSE）和结构相似性（SSIM）指标对比所有方法性能，提出一种新型运动校正流程，能有效去除干扰运动，同时完整保留吞咽相关动态，超越现有竞争技术。
原文摘要 · Abstract (English)
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.
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