无需标记物，实时精准追踪放疗中肿瘤运动，精度超0.8毫米。
A Novel Automatic Real-time Motion Tracking Method in MRI-guided Radiotherapy Using Enhanced Tracking-Learning-Detection Framework with Automatic Segmentation
- 融合增强TLD与改进的Chan-Vese模型，实现自动分割与实时追踪。
- 106,000帧评估中，追踪误差<0.8mm，精度超99%，召回率98%。
- 适用于肝转移患者，适合临床实时放疗系统部署。
在磁共振引导放疗（MRIgRT）中，精确运动追踪对有效治疗至关重要。本研究提出一种基于增强追踪-学习-检测（ETLD）框架与改进的Chan-Vese模型（ICV）融合的新方法（ETLD+ICV），实现自动实时无标记追踪。通过优化图像预处理、无参考质量评估、增强中值流追踪器及动态搜索区域调整的检测器，提升实时电影影像追踪能力；结合追踪结果逐帧优化分割区域，关键参数经调优。在10例肝转移患者的3.5D MRI扫描中共测试77个治疗分次，共106,000帧。结果显示追踪误差低于0.8毫米，所有受试者在束眼视图（BEV）/束路径视图（BPV）下的精度超过99%，召回率高达98%；全局Dice评分均超过82%，表明方法具备良好扩展性与精准靶区覆盖能力。该方法显著优于现有技术，兼具高精度与临床适应性，可显著提升放疗疗效。
原文摘要 · Abstract (English)
Background and Purpose: Accurate motion tracking in MRI-guided Radiotherapy (MRIgRT) is essential for effective treatment delivery. This study aimed to enhance motion tracking precision in MRIgRT through an automatic real-time markerless tracking method using an enhanced Tracking-Learning-Detection (ETLD) framework with automatic segmentation. Materials and Methods: We developed a novel MRIgRT motion tracking and segmentation method by integrating the ETLD framework with an improved Chan-Vese model (ICV), named ETLD+ICV. The ETLD framework was upgraded for real-time cine MRI, including advanced image preprocessing, no-reference image quality assessment, an enhanced median-flow tracker, and a refined detector with dynamic search region adjustments. ICV was used for precise target volume coverage, refining the segmented region frame by frame using tracking results, with key parameters optimized. The method was tested on 3.5D MRI scans from 10 patients with liver metastases. Results: Evaluation of 106,000 frames across 77 treatment fractions showed sub-millimeter tracking errors of less than 0.8mm, with over 99% precision and 98% recall for all subjects in the Beam Eye View(BEV)/Beam Path View(BPV) orientation. The ETLD+ICV method achieved a dice global score of more than 82% for all subjects, demonstrating the method's extensibility and precise target volume coverage. Conclusion: This study successfully developed an automatic real-time markerless motion tracking method for MRIgRT that significantly outperforms current methods. The novel method not only delivers exceptional precision in tracking and segmentation but also shows enhanced adaptability to clinical demands, making it an indispensable asset in improving the efficacy of radiotherapy treatments.
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