Shape of my heart: Cardiac models through learned signed distance functions

• URL: http://arxiv.org/abs/2308.16568v4
• Date: Wed, 22 May 2024 13:35:03 GMT
• Title: Shape of my heart: Cardiac models through learned signed distance functions
• Authors: Jan Verhülsdonk, Thomas Grandits, Francisco Sahli Costabal, Thomas Pinetz, Rolf Krause, Angelo Auricchio, Gundolf Haase, Simone Pezzuto, Alexander Effland,
• Abstract summary: 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.
• Score: 33.29148402516714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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