用心电图生成心脏影像，提升疾病分类准确率
ECGFlowCMR: Pretraining with ECG-Generated Cine CMR Helps Cardiac Disease Classification and Phenotype Prediction
- 通过心电图引导生成心脏动态影像，解决时序与结构不匹配问题
- 在英国生物银行和临床数据集上显著提升疾病分类性能
- 适合医学影像生成与心脏病智能诊断研究者参考
心脏磁共振（CMR）成像可全面评估心脏结构与功能，但受限于高成本和对专家标注的依赖，难以获得大规模标注数据集。相比之下，心电图（ECG）成本低、普及广，有望用于引导动态CMR的生成。为此，我们提出ECGFlowCMR，一种新型的ECG-to-CMR生成框架，融合相位感知掩码自编码器（PA-MAE）与解耦解剖-运动流模型（AMDF），以应对两大核心挑战：(1) 多搏动ECG记录与单周期CMR序列间的跨模态时间错配；(2) 由于ECG固有结构信息有限导致的解剖可观测性缺口。在英国生物银行及自有临床数据集上的大量实验表明，ECGFlowCMR可从ECG输入生成逼真的动态CMR序列，支持可扩展预训练，并显著提升下游心脏疾病分类与表型预测任务的表现。
原文摘要 · Abstract (English)
Cardiac Magnetic Resonance (CMR) imaging provides a comprehensive assessment of cardiac structure and function but remains constrained by high acquisition costs and reliance on expert annotations, limiting the availability of large-scale labeled datasets. In contrast, electrocardiograms (ECGs) are inexpensive, widely accessible, and offer a promising modality for conditioning the generative synthesis of cine CMR. To this end, we propose ECGFlowCMR, a novel ECG-to-CMR generative framework that integrates a Phase-Aware Masked Autoencoder (PA-MAE) and an Anatomy-Motion Disentangled Flow (AMDF) to address two fundamental challenges: (1) the cross-modal temporal mismatch between multi-beat ECG recordings and single-cycle CMR sequences, and (2) the anatomical observability gap due to the limited structural information inherent in ECGs. Extensive experiments on the UK Biobank and a proprietary clinical dataset demonstrate that ECGFlowCMR can generate realistic cine CMR sequences from ECG inputs, enabling scalable pretraining and improving performance on downstream cardiac disease classification and phenotype prediction tasks.
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