Cross-Modality Cardiac Insight Transfer: A Contrastive Learning Approach to Enrich ECG with CMR Features

Cardiovascular diseases are the leading cause of death worldwide, and accurate diagnostic tools are crucial for their early detection and treatment. Electrocardiograms (ECG) offer a non-invasive and widely accessible diagnostic method. Despite their convenience, they are limited in providing in-depth cardiovascular information. On the other hand, Cardiac Magnetic Resonance Imaging (CMR) can reveal detailed structural and functional heart information; however, it is costly and not widely accessible. This study aims to bridge this gap through a contrastive learning framework that deeply integrates ECG data with insights from CMR, allowing the extraction of cardiovascular information solely from ECG. We developed an innovative contrastive learning algorithm trained on a large-scale paired ECG and CMR dataset, enabling ECG data to map onto the feature space of CMR data. Experimental results demonstrate that our method significantly improves the accuracy of cardiovascular disease diagnosis using only ECG data. Furthermore, our approach enhances the correlation coefficient for predicting cardiac traits from ECG, revealing potential connections between ECG and CMR. This study not only proves the effectiveness of contrastive learning in cross-modal medical image analysis but also offers a low-cost, efficient way to leverage existing ECG equipment for a deeper understanding of cardiovascular health conditions. Our code is available at https://github.com/Yukui-1999/ECCL .