用CMR信息增强心电图表征，提升心脏功能预测能力
Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG
- 通过患者级与时间级对比学习，融合心电图与心脏磁共振数据
- 在27,951例数据上，显著提升心脏表型检索与功能参数预测性能
- 无需新增可学习参数，适合临床心电图智能诊断场景
心电图（ECG）是检测心脏电活动异常的常用且低成本工具，但无法直接测量心室容积和射血分数等关键功能参数。心脏磁共振（CMR）虽为金标准，提供详细结构与功能信息，却成本高、难普及。为弥合这一差距，我们提出PTACL（患者与时间对齐对比学习）框架，通过整合CMR的时空信息增强心电图表征。该方法采用全局患者级对比损失与局部时间级对比损失：前者拉近同一患者的ECG与CMR嵌入，推开不同患者间嵌入；后者在每个患者内对比编码后的ECG片段与对应CMR帧，实现细粒度时间对齐。此策略使心电图表征包含超越电活动的诊断信息，并比仅全局对齐传递更多模态间知识，且不引入新可学习参数。我们在英国生物银行的27,951名受试者配对ECG-CMR数据上评估，相比基线方法，PTACL在两项临床相关任务中表现更优：（1）检索具有相似心脏表型的患者；（2）预测基于CMR的心脏功能参数，如心室容积与射血分数。结果表明PTACL有潜力通过心电图提升非侵入性心脏诊断水平。代码已开源：https://github.com/alsalivan/ecgcmr
原文摘要 · Abstract (English)
An electrocardiogram (ECG) is a widely used, cost-effective tool for detecting electrical abnormalities in the heart. However, it cannot directly measure functional parameters, such as ventricular volumes and ejection fraction, which are crucial for assessing cardiac function. Cardiac magnetic resonance (CMR) is the gold standard for these measurements, providing detailed structural and functional insights, but is expensive and less accessible. To bridge this gap, we propose PTACL (Patient and Temporal Alignment Contrastive Learning), a multimodal contrastive learning framework that enhances ECG representations by integrating spatio-temporal information from CMR. PTACL uses global patient-level contrastive loss and local temporal-level contrastive loss. The global loss aligns patient-level representations by pulling ECG and CMR embeddings from the same patient closer together, while pushing apart embeddings from different patients. Local loss enforces fine-grained temporal alignment within each patient by contrasting encoded ECG segments with corresponding encoded CMR frames. This approach enriches ECG representations with diagnostic information beyond electrical activity and transfers more insights between modalities than global alignment alone, all without introducing new learnable weights. We evaluate PTACL on paired ECG-CMR data from 27,951 subjects in the UK Biobank. Compared to baseline approaches, PTACL achieves better performance in two clinically relevant tasks: (1) retrieving patients with similar cardiac phenotypes and (2) predicting CMR-derived cardiac function parameters, such as ventricular volumes and ejection fraction. Our results highlight the potential of PTACL to enhance non-invasive cardiac diagnostics using ECG. The code is available at: https://github.com/alsalivan/ecgcmr
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