心之形：通过学习的符号距离函数构建心脏模型
Shape of my heart: Cardiac models through learned signed distance functions
AI总结：
针对心脏解剖模型构建效率低、迁移性差的问题，提出基于Lipschitz正则三维深度符号距离函数的心脏形状重建方法，能从部分数据或不同模态重建解剖模型。
摘要：
解剖模型的高效构建是患者特异性人体心脏在体（in-silico）模型的主要挑战之一。当前方法常依赖线性统计模型，无法支持高级拓扑变化，或需要医学图像分割后接网格生成流程，而这强烈依赖于图像分辨率、质量和模态。因此，这些方法在向其他成像域迁移时受限。在本工作中，通过具有Lipschitz正则性的三维深度符号距离函数重建心脏形状。为此，学习心脏MRI重建的形状以建模多个心腔的空间关系。我们证明该方法还能从部分数据（如单个心室的点云）或不同于训练所用MRI的模态（如电解剖标测（EAM））重建解剖模型。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
