A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction

• URL: http://arxiv.org/abs/2102.07899v1
• Date: Tue, 16 Feb 2021 00:39:43 GMT
• Title: A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction
• Authors: Fanwei Kong, Nathan Wilson, Shawn C. Shadden
• Abstract summary: We propose a novel deep-learning-based approach that directly predicts whole heart surface meshes from volumetric CT and MR image data. Our method demonstrated promising performance of generating high-resolution and high-quality whole heart reconstructions.
• Score: 1.8047694351309207
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated construction of surface geometries of cardiac structures from volumetric medical images is important for a number of clinical applications. While deep-learning based approaches have demonstrated promising reconstruction precision, these approaches have mostly focused on voxel-wise segmentation followed by surface reconstruction and post-processing techniques. However, such approaches suffer from a number of limitations including disconnected regions or incorrect surface topology due to erroneous segmentation and stair-case artifacts due to limited segmentation resolution. We propose a novel deep-learning-based approach that directly predicts whole heart surface meshes from volumetric CT and MR image data. Our approach leverages a graph convolutional neural network to predict deformation on mesh vertices from a pre-defined mesh template to reconstruct multiple anatomical structures in a 3D image volume. Our method demonstrated promising performance of generating high-resolution and high-quality whole heart reconstructions and outperformed prior deep-learning based methods on both CT and MR data in terms of precision and surface quality. Furthermore, our method can more efficiently produce temporally-consistent and feature-corresponding surface mesh predictions for heart motion from CT or MR cine sequences, and therefore can potentially be applied for efficiently constructing 4D whole heart dynamics.

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