CTSL：基于码本的时空学习，用于利用 Cine MRI 进行准确的非造影心脏风险预测
CTSL: Codebook-based Temporal-Spatial Learning for Accurate Non-Contrast Cardiac Risk Prediction Using Cine MRIs
- Fudan University, Shanghai, China（复旦大学）
- Shanghai Innovation Institute, Shanghai, China（上海创新研究院）
- Shanghai Jiao Tong University, Shanghai, China（上海交通大学）
- Shanghai Artificial Intelligence Laboratory, Shanghai, China（上海人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出自监督 CTSL 框架，利用码本表征、多视图蒸馏和运动线索自检测，从无掩膜 Cine MRI 中学习时空特征，实现优于造影依赖方法的非侵入式 MACE 风险预测。
AI中文摘要：
从 Cine MRI 序列中准确且无需造影地预测主要不良心脏事件（MACE）仍是一项关键挑战。现有方法通常需要基于人工细化的心室心肌掩膜进行监督学习，而在没有造影剂的情况下这些方法并不实用。我们提出一个自监督框架，即基于码本的时空学习（Codebook-based Temporal-Spatial Learning，CTSL），它可直接从原始 Cine 数据中学习动态时空表征，而不需要分割掩膜。CTSL 通过多视图蒸馏策略解耦时间与空间特征：教师模型处理多个 Cine 视图，学生模型则从降维的 Cine-SA 序列中学习。通过利用基于码本的特征表征以及借助运动线索实现的动态病灶自检测，CTSL 捕捉复杂的时间依赖关系和运动模式。我们的模型实现了高置信度的 MACE 风险预测，提供了一种快速、非侵入性的心脏风险评估方案，其表现优于传统依赖造影的方法，从而能够在临床环境中实现及时且可获得的心脏病诊断。
英文摘要：
Accurate and contrast-free Major Adverse Cardiac Events (MACE) prediction from Cine MRI sequences remains a critical challenge. Existing methods typically necessitate supervised learning based on human-refined masks in the ventricular myocardium, which become impractical without contrast agents. We introduce a self-supervised framework, namely Codebook-based Temporal-Spatial Learning (CTSL), that learns dynamic, spatiotemporal representations from raw Cine data without requiring segmentation masks. CTSL decouples temporal and spatial features through a multi-view distillation strategy, where the teacher model processes multiple Cine views, and the student model learns from reduced-dimensional Cine-SA sequences. By leveraging codebook-based feature representations and dynamic lesion self-detection through motion cues, CTSL captures intricate temporal dependencies and motion patterns. High-confidence MACE risk predictions are achieved through our model, providing a rapid, non-invasive solution for cardiac risk assessment that outperforms traditional contrast-dependent methods, thereby enabling timely and accessible heart disease diagnosis in clinical settings.
