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Cardiovascular diseases (CVDs) are the leading cause of global mortality,necessitating accessible and accurate diagnostic tools. While cardiac magneticresonance imaging (CMR) provides gold-standard insights into cardiac structureand function, its clinical utility is limited by high cost and complexity. Incontrast, electrocardiography (ECG) is inexpensive and widely available butlacks the granularity of CMR. We propose CardioNets, a deep learning frameworkthat translates 12-lead ECG signals into CMR-level functional parameters andsynthetic images, enabling scalable cardiac assessment. CardioNets integratescross-modal contrastive learning and generative pretraining, aligning ECG withCMR-derived cardiac phenotypes and synthesizing high-resolution CMR images viaa masked autoregressive model. Trained on 159,819 samples from five cohorts,including the UK Biobank (n=42,483) and MIMIC-IV-ECG (n=164,550), andexternally validated on independent clinical datasets (n=3,767), CardioNetsachieved strong performance across disease screening and phenotype estimationtasks. In the UK Biobank, it improved cardiac phenotype regression R2 by 24.8%and cardiomyopathy AUC by up to 39.3% over baseline models. In MIMIC, itincreased AUC for pulmonary hypertension detection by 5.6%. Generated CMRimages showed 36.6% higher SSIM and 8.7% higher PSNR than prior approaches. Ina reader study, ECG-only CardioNets achieved 13.9% higher accuracy than humanphysicians using both ECG and real CMR. These results suggest that CardioNetsoffers a promising, low-cost alternative to CMR for large-scale CVD screening,particularly in resource-limited settings. Future efforts will focus onclinical deployment and regulatory validation of ECG-based synthetic imaging.

Translating Electrocardiograms to Cardiac Magnetic Resonance Imaging  Useful for Cardiac Assessment and Disease Screening: A Multi-Center Study AI  for ECG to CMR Translation Study