无需对比剂和标注，用自监督学习预测心脏风险
CTSL: Codebook-based Temporal-Spatial Learning for Accurate Non-Contrast Cardiac Risk Prediction Using Cine MRIs
- 通过多视角蒸馏分离时空特征，从原始动态MRI中学习
- 在无标注情况下实现高精度心脏事件风险预测
- 适合临床快速筛查，无需造影剂和人工分割
从动态心脏磁共振（Cine MRI）序列中准确、无对比剂地预测主要不良心血管事件（MACE）仍是重大挑战。现有方法通常依赖心肌区域的人工标注掩膜，但在无对比剂时难以实施。本文提出一种自监督框架——基于码本的时空学习（CTSL），可直接从原始Cine数据中学习动态时空表征，无需分割掩膜。CTSL采用多视角蒸馏策略，教师模型处理多个Cine视图，学生模型则从降维后的Cine-SA序列中学习。结合码本特征表示与基于运动线索的动态病灶自检测机制，模型有效捕捉复杂的时序依赖性和运动模式。最终实现高置信度的MACE风险预测，提供一种快速、无创的心脏风险评估方案，性能优于传统依赖对比剂的方法，有助于临床环境中实现及时、可及的心脏病诊断。
原文摘要 · Abstract (English)
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.
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