The efficient construction of anatomical models is one of the major challenges of patient-specific in-silico models of the human heart. Current methods frequently rely on linear statistical models, allowing no advanced topological changes, or requiring medical image segmentation followed by a meshing pipeline, which strongly depends on image resolution, quality, and modality. These approaches are therefore limited in their transferability to other imaging domains. In this work, the cardiac shape is reconstructed by means of three-dimensional deep signed distance functions with Lipschitz regularity. For this purpose, the shapes of cardiac MRI reconstructions are learned to model the spatial relation of multiple chambers. We demonstrate that this approach is also capable of reconstructing anatomical models from partial data, such as point clouds from a single ventricle, or modalities different from the trained MRI, such as the electroanatomical mapping (EAM).

翻译：解剖模型的高效构建是面向患者特异性人心脏在体仿真模型的主要挑战之一。现有方法通常依赖线性统计模型，无法实现高级拓扑变化，或需在医学图像分割后执行网格化流程，而这严重依赖于图像分辨率、质量与模态。因此，这些方法在向其他成像领域迁移时存在局限性。本研究通过具有Lipschitz正则性的三维深度有符号距离函数重建心脏形状。为此，我们学习心脏MRI重建的形状，以建模多个心腔间的空间关系。研究表明，该方法还能从部分数据（如单心室点云）或与训练MRI不同的模态（如电解剖标测EAM）中重建解剖模型。