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：高效构建解剖模型是面向患者特异性的人类心脏硅内模型面临的主要挑战之一。现有方法通常依赖于线性统计模型，无法实现高级拓扑结构变化，或需要先进行医学图像分割再执行网格生成流程，这严重受限于图像分辨率、质量与模态。因此，这些方法在跨影像领域的可迁移性方面存在局限。本研究通过具有Lipschitz正则性的三维深度符号距离函数实现心脏形状重建。为此，我们通过学习心脏磁共振成像重建的形状来建模多心腔的空间关系。实验证明，该方法还能从局部数据（如单心室点云）或与训练所用磁共振成像不同的模态（如电解剖标测）中重建解剖模型。