Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases with associated clinical decision-making typically based on single-valued imaging biomarkers. However, such metrics only approximate the complex 3D structure and physiology of the heart and hence hinder a better understanding and prediction of MI outcomes. In this work, we investigate the utility of complete 3D cardiac shapes in the form of point clouds for an improved detection of MI events. To this end, we propose a fully automatic multi-step pipeline consisting of a 3D cardiac surface reconstruction step followed by a point cloud classification network. Our method utilizes recent advances in geometric deep learning on point clouds to enable direct and efficient multi-scale learning on high-resolution surface models of the cardiac anatomy. We evaluate our approach on 1068 UK Biobank subjects for the tasks of prevalent MI detection and incident MI prediction and find improvements of ~13% and ~5% respectively over clinical benchmarks. Furthermore, we analyze the role of each ventricle and cardiac phase for 3D shape-based MI detection and conduct a visual analysis of the morphological and physiological patterns typically associated with MI outcomes.

翻译：心肌梗死是最常见的心血管疾病之一，其临床决策通常依赖于单值影像生物标志物。然而，这类指标仅能近似反映心脏复杂的3D结构和生理机能，因而阻碍了对心肌梗死结果的理解与预测。本研究探讨了以点云形式呈现的完整3D心脏形状在改进心肌梗死事件检测中的效用。为此，我们提出了一种全自动多步骤流程，包含3D心脏表面重建步骤和点云分类网络。该方法利用了几何深度学习在点云上的最新进展，能够直接在心脏解剖结构的高分辨率表面模型上进行高效多尺度学习。我们在1068名UK Biobank受试者上评估了该方法在现有心肌梗死检测和未来心肌梗死预测任务中的表现，发现相较于临床基准指标分别提升了约13%和约5%。此外，我们分析了每个心室和心脏相位在基于3D形状的心肌梗死检测中的作用，并对与心肌梗死结果相关的形态学和生理学模式进行了视觉分析。