发表机构
Pattern Recognition Lab, Friedrich Alexander University（模式识别实验室，弗赖堡亚历山大大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无心电图动态冠状动脉造影中准确心脏相位匹配和导管尖端跟踪的挑战，提出统一框架，利用大规模时空编码器预训练学习心脏运动动力学，引入辅助任务并采用多数投票后处理策略，实现高精度实时引导，性能达先进水平。
AI 中文摘要
经皮冠状动脉介入治疗（PCI）用于恢复因动脉粥样硬化斑块阻塞的冠状动脉血流，期间需反复注射碘造影剂，这会增加辐射暴露和造影剂肾病风险。动态冠状动脉造影（DRM）可降低这些风险，但在无心电图设置且手动标注有限时，准确的心脏相位匹配和导管尖端跟踪具有挑战性。我们提出一个统一的DRM框架，同时进行心脏相位匹配和导管尖端跟踪以实现精确实时引导。该方法采用在1600万X射线帧上预训练的大规模时空编码器学习心脏运动动力学，这是首次将大规模时空预训练应用于DRM运动补偿。还引入基于心电图R波检测和导管尖端跟踪的辅助任务，改进优化并减少对大量导管掩码标注的需求。最后，多数投票后处理策略聚合时间预测，提高鲁棒性并提供与相位匹配误差相关的置信度得分。在临床X射线数据集上的综合评估显示了该模型的性能，实现了低时间错位和适用于实时DRM的稳健相位匹配精度。
英文摘要
Percutaneous Coronary Intervention (PCI) is a minimally invasive procedure used to restore coronary blood flow obstructed by atherosclerotic plaque. During PCI, repeated injections of iodine-based contrast agents are required to visualize the coronary arteries and guide interventional devices. However, frequent contrast injections increase radiation exposure and the risk of contrast-induced nephropathy, with acute kidney injury reported in up to 30% of patients with renal impairment. Dynamic Coronary Roadmapping (DRM) reduces these risks by overlaying a precomputed angiographic vessel map onto live fluoroscopy and continuously updating it throughout the procedure. Accurate DRM relies on precise cardiac phase matching between angiography and fluoroscopy, together with reliable catheter tip tracking for motion compensation. These tasks remain challenging in ECG-free settings and when only limited manual annotations are available. We present a unified DRM framework that simultaneously performs cardiac phase matching and catheter tip tracking for accurate real-time guidance. Our method employs a large-scale spatio-temporal encoder pretrained on 16 million X-ray frames to learn cardiac motion dynamics. To the best of our knowledge, this is the first application of large-scale spatio-temporal pretraining for motion compensation in DRM. We further introduce auxiliary tasks based on ECG R-peak detection and catheter tip tracking, improving optimization while eliminating the need for extensive catheter mask annotations. Finally, a majority-voting postprocessing strategy aggregates temporal predictions, improving robustness and providing a confidence score that correlates with phase-matching error. Comprehensive evaluation on clinical X-ray datasets demonstrates state-of-the-art performance, achieving low temporal misalignment and robust phase-matching accuracy suitable for real-time DRM.
Comments13 pages, 10 figures
