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.

通过三维深度有限函数和立体雅可比技术，我们可以高效地重建患者特异性的心脏解剖模型，从而在其他医学成像领域中具有广泛应用。