一种用于电影心脏MRI时空建模的潜在ODE方法
A Latent ODE Approach to Spatiotemporal Modeling of Cine Cardiac MRI
- Swiss Data Science Center, EPFL and ETH Zürich（瑞士数据科学中心，EPFL 和 ETH 苏黎世）
- Diagnostic and Interventional Radiology, University Hospital Zürich（诊断与介入放射学，苏黎世大学医院）
- Institute for Biomedical Engineering, University and ETH Zürich（生物医学工程研究所，苏黎世大学和 ETH 苏黎世）
- Biomedical Informatics Group, ETH Zürich（生物医学信息学小组，ETH 苏黎世）
- Division of Preventive Medicine, Brigham and Women’s Hospital / Harvard Medical School（预防医学部，布莱根妇女医院/哈佛医学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出一种基于神经ODE的潜在动力学模型，从心脏MRI中编码双心室结构和全周期运动，通过图网格自编码器重建3D+t运动，并利用Cox模型评估偏离预期状态对心衰的预测能力，在UK Biobank数据上优于传统指标。
AI中文摘要：
心脏磁共振成像（CMR）捕获了关于心室结构和运动的丰富时空信息，但传统风险模型仅使用来自选定心脏阶段的少数图像衍生指标。我们提出了一种潜在动力学模型，将双心室解剖结构和全周期电影运动编码为连续潜在轨迹，使用心率感知的神经常微分方程（ODE）动力学和基于图的网格自编码器来重建解剖一致性的3D+t心室运动。协变量条件先验定义了预期的舒张末期潜在状态，Cox比例风险模型测试偏离该先验是否能预测心衰事件。我们研究了72,386名无基线心血管疾病的UK Biobank参与者，包括367例心衰事件。在保留的评估子集中，将潜在评分添加到重新拟合的汇集队列方程中，将分层C指数从0.704提高到0.785，而七个既定心脏标志物为0.764。与非图和非ODE方法相比，所提出的模型在重建保真度、生成真实性和下游预后性能之间提供了最佳权衡。这些结果表明，连续全周期心室运动建模提供了超越传统CMR总结的信息性心脏表型，但在用于临床风险预测之前，需要在更具代表性的患者队列中进行外部验证。
英文摘要：
Cardiac magnetic resonance imaging (CMR) captures rich spatiotemporal information about ventricular structure and motion, but conventional risk models use only a few image-derived indices from selected cardiac phases. We present a latent dynamical model that encodes bi-ventricular anatomy and full-cycle cine motion as a continuous latent trajectory, using heart-rate-aware neural ordinary differential equation (ODE) dynamics and a graph-based mesh autoencoder to reconstruct anatomically consistent 3D+t ventricular motion. A covariate-conditioned prior defines the expected end-diastolic latent state, and a Cox proportional hazards model tests whether deviations from this prior predict incident heart failure. We studied 72,386 UK Biobank participants without baseline cardiovascular disease, including 367 incident heart failure events. In a held-out evaluation subset, adding the latent score to refitted pooled cohort equations improved the stratified C-index from 0.704 to 0.785, compared with 0.764 for seven established cardiac markers. Compared with non-graph and non-ODE approaches, the proposed model gave the best trade-off between reconstruction fidelity, generative realism, and downstream prognostic performance. These results suggest that continuous full-cycle modeling of ventricular motion provides informative cardiac phenotypes beyond conventional CMR summaries, while external validation in more representative patient cohorts is required before clinical risk-prediction use.
