The efficient construction of an anatomical model 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 from public databases to model the spatial relation of multiple chambers in Cartesian space. 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 electroanatomical mapping, and in addition, allows us to generate new anatomical shapes by randomly sampling latent vectors.

翻译：解剖模型的高效构建是患者特异性人类心脏计算机模型面临的主要挑战之一。当前方法通常依赖线性统计模型，无法实现高级拓扑变化，或需要医学图像分割及后续的网格化流程，而这强烈依赖于图像分辨率、质量和模态。因此，这些方法在转移至其他成像领域时存在局限性。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，我们从公共数据库中学习心脏MRI重建的形状，以建模笛卡尔空间中多个腔室的空间关系。我们证明，该方法还能从部分数据（如单个心室点云）或不同于训练MRI的模态（如电解剖标测）重建解剖模型，此外，通过随机采样潜在向量，还可生成新的解剖形状。