用心电图生成心脏核磁图像，低成本实现大规模心脏病筛查
Translating Electrocardiograms to Cardiac Magnetic Resonance Imaging Useful for Cardiac Assessment and Disease Screening: A Multi-Center Study
- 通过深度学习将心电图转化为心脏核磁功能参数和合成图像
- 在多个数据集上显著提升心脏病检测准确率，合成图像质量优于以往方法
- 适合资源有限地区推广，可替代部分核磁检查用于早期筛查
心血管疾病是全球死亡主因，亟需可及且精准的诊断工具。尽管心脏磁共振（CMR）能提供心脏结构与功能的金标准信息，但其临床应用受限于高成本与复杂性。相比之下，心电图（ECG）价格低廉、普及度高，但缺乏CMR的精细解析能力。本文提出CardioNets，一种深度学习框架，可将12导联心电图信号转换为具备CMR级别功能参数和合成图像，实现可扩展的心脏评估。CardioNets融合跨模态对比学习与生成预训练，对齐心电图与CMR衍生的心脏表型，并通过掩码自回归模型生成高分辨率CMR图像。模型基于五个队列共159,819个样本训练，包括英国生物银行（n=42,483）和MIMIC-IV-ECG（n=164,550），并在独立临床数据集（n=3,767）上外部验证。在英国生物银行中，其心脏表型回归的R²提升24.8%，心肌病的AUC最高提升39.3%；在MIMIC中，肺动脉高压检测的AUC提升5.6%。生成的CMR图像比之前方法的SSIM高36.6%，PSNR高8.7%。读者研究显示，仅使用心电图的CardioNets准确率比依赖真实CMR的人类医生高出13.9%。结果表明，CardioNets为大规模心血管疾病筛查提供了有前景的低成本替代方案，尤其适用于资源匮乏地区。未来工作将聚焦于临床部署与监管验证。
原文摘要 · Abstract (English)
Cardiovascular diseases (CVDs) are the leading cause of global mortality, necessitating accessible and accurate diagnostic tools. While cardiac magnetic resonance imaging (CMR) provides gold-standard insights into cardiac structure and function, its clinical utility is limited by high cost and complexity. In contrast, electrocardiography (ECG) is inexpensive and widely available but lacks the granularity of CMR. We propose CardioNets, a deep learning framework that translates 12-lead ECG signals into CMR-level functional parameters and synthetic images, enabling scalable cardiac assessment. CardioNets integrates cross-modal contrastive learning and generative pretraining, aligning ECG with CMR-derived cardiac phenotypes and synthesizing high-resolution CMR images via a 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), and externally validated on independent clinical datasets (n=3,767), CardioNets achieved strong performance across disease screening and phenotype estimation tasks. 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, it increased AUC for pulmonary hypertension detection by 5.6%. Generated CMR images showed 36.6% higher SSIM and 8.7% higher PSNR than prior approaches. In a reader study, ECG-only CardioNets achieved 13.9% higher accuracy than human physicians using both ECG and real CMR. These results suggest that CardioNets offers a promising, low-cost alternative to CMR for large-scale CVD screening, particularly in resource-limited settings. Future efforts will focus on clinical deployment and regulatory validation of ECG-based synthetic imaging.
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