Shape of my heart: Cardiac models through learned signed distance functions
This work addresses the problem of limited transferability and dependency on image quality in cardiac modeling for medical applications, representing an incremental improvement over existing methods.
The paper tackles the challenge of constructing patient-specific cardiac anatomical models by introducing a method using 3D deep signed distance functions with Lipschitz regularity, which can reconstruct shapes from partial data like point clouds or different imaging modalities such as electroanatomical mapping, demonstrating transferability across domains.
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).