Cardiac magnetic resonance imaging (CMR) offers detailed evaluation of cardiac structure and function, but its limited accessibility restricts use to selected patient populations. In contrast, the electrocardiogram (ECG) is ubiquitous and inexpensive, and provides rich information on cardiac electrical activity and rhythm, yet offers limited insight into underlying cardiac structure and mechanical function. To address this, we introduce a contrastive learning framework that improves the extraction of clinically relevant cardiac phenotypes from ECG by learning from paired ECG-CMR data. Our approach aligns ECG representations with 3D CMR volumes at end-diastole (ED) and end-systole (ES), with a dual-phase contrastive loss to anchor each ECG jointly with both cardiac phases in a shared latent space. Unlike prior methods limited to 2D CMR representations with or without a temporal component, our framework models 3D anatomy at both ED and ES phases as distinct latent representations, enabling flexible disentanglement of structural and functional cardiac properties. Using over 34,000 ECG-CMR pairs from the UK Biobank, we demonstrate improved extraction of image-derived phenotypes from ECG, particularly for functional parameters ($\uparrow$ 9.2\%), while improvements in clinical outcome prediction remained modest ($\uparrow$ 0.7\%). This strategy could enable scalable and cost-effective extraction of image-derived traits from ECG. The code for this research is publicly available.

翻译：心脏磁共振成像（CMR）可提供对心脏结构与功能的详细评估，但其有限的可及性使其仅能用于特定患者群体。相比之下，心电图（ECG）普及度高且成本低廉，能提供关于心脏电活动与节律的丰富信息，但对潜在的心脏结构与机械功能洞察有限。为解决此问题，我们引入了一种对比学习框架，该框架通过学习配对的ECG-CMR数据，改进了从ECG中提取临床相关心脏表型的能力。我们的方法将ECG表征与舒张末期（ED）和收缩末期（ES）的3D CMR体积对齐，采用双阶段对比损失，将每个ECG与两个心脏阶段共同锚定在一个共享的潜在空间中。与先前仅限于带或不带时间分量的2D CMR表征的方法不同，我们的框架将ED和ES阶段的3D解剖结构建模为不同的潜在表征，从而能够灵活地解耦心脏的结构与功能特性。利用来自英国生物银行的超过34,000对ECG-CMR数据，我们证明了从ECG中提取影像衍生表型的能力得到提升，尤其是功能参数（$\uparrow$ 9.2%），而临床结局预测的改善则较为有限（$\uparrow$ 0.7%）。该策略有望实现从ECG中可扩展且经济高效地提取影像衍生特征。本研究的代码已公开。