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

TL;DR

This paper introduces a novel method for reconstructing cardiac anatomical models using deep signed distance functions, enabling flexible, topology-aware, and modality-agnostic heart shape modeling from various data sources.

Contribution

The work presents a deep learning approach with signed distance functions for cardiac shape reconstruction that handles partial data and different imaging modalities, surpassing traditional methods.

Findings

Accurately reconstructs cardiac shapes from MRI data.

Capable of modeling from partial point clouds.

Works across different imaging modalities like EAM.

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).