用无钆增强的动态心脏磁共振影像，精准筛查心肌病。
LGE-Guided Cross-Modality Contrastive Learning for Gadolinium-Free Cardiomyopathy Screening in Cine CMR
- 通过对比学习对齐动态与延迟钆增强图像的特征空间。
- 在231人多中心数据上达到94.3%准确率，比现有模型高4.3%。
- 无需钆剂、适应性强，适合广泛临床环境使用。
心肌病是心力衰竭和突发性心脏死亡的主要诱因，亟需精准早期筛查。心脏磁共振（CMR）作为多参数诊断‘金标准’，具备良好筛查潜力，但依赖钆对比剂且人工解读耗时，限制了大规模应用。本文提出CC-CMR框架，一种基于对比学习与跨模态对齐的无钆心肌病筛查方法，利用动态CMR序列与延迟钆增强（LGE）序列的特征空间对齐，将纤维化病理信息编码至动态图像嵌入中。特征交互模块同步优化诊断精度与跨模态一致性，并引入不确定性引导的自适应训练机制，动态调整任务目标以提升模型泛化能力。在包含231名受试者的多中心数据上，CC-CMR实现0.943的准确率（95%置信区间：0.886–0.986），较当前最优纯动态CMR模型提升4.3%，且完全消除钆依赖，证明其在多种人群与医疗环境中具有临床可行性。
原文摘要 · Abstract (English)
Cardiomyopathy, a principal contributor to heart failure and sudden cardiac mortality, demands precise early screening. Cardiac Magnetic Resonance (CMR), recognized as the diagnostic 'gold standard' through multiparametric protocols, holds the potential to serve as an accurate screening tool. However, its reliance on gadolinium contrast and labor-intensive interpretation hinders population-scale deployment. We propose CC-CMR, a Contrastive Learning and Cross-Modal alignment framework for gadolinium-free cardiomyopathy screening using cine CMR sequences. By aligning the latent spaces of cine CMR and Late Gadolinium Enhancement (LGE) sequences, our model encodes fibrosis-specific pathology into cine CMR embeddings. A Feature Interaction Module concurrently optimizes diagnostic precision and cross-modal feature congruence, augmented by an uncertainty-guided adaptive training mechanism that dynamically calibrates task-specific objectives to ensure model generalizability. Evaluated on multi-center data from 231 subjects, CC-CMR achieves accuracy of 0.943 (95% CI: 0.886-0.986), outperforming state-of-the-art cine-CMR-only models by 4.3% while eliminating gadolinium dependency, demonstrating its clinical viability for wide range of populations and healthcare environments.
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