Learning Whole Heart Mesh Generation From Patient Images For Computational Simulations
This work addresses the need for efficient patient-specific cardiac modeling in medical imaging and computational simulations, representing an incremental improvement over existing methods.
The paper tackles the problem of generating simulation-suitable heart meshes from patient images, which traditionally requires complex manual effort, by presenting a fast, automated deep-learning method that deforms a template mesh; it achieves promising accuracy, outperforms prior methods in reconstruction, and produces more consistent geometries for cardiac flow simulations.
Patient-specific cardiac modeling combines geometries of the heart derived from medical images and biophysical simulations to predict various aspects of cardiac function. However, generating simulation-suitable models of the heart from patient image data often requires complicated procedures and significant human effort. We present a fast and automated deep-learning method to construct simulation-suitable models of the heart from medical images. The approach constructs meshes from 3D patient images by learning to deform a small set of deformation handles on a whole heart template. For both 3D CT and MR data, this method achieves promising accuracy for whole heart reconstruction, consistently outperforming prior methods in constructing simulation-suitable meshes of the heart. When evaluated on time-series CT data, this method produced more anatomically and temporally consistent geometries than prior methods, and was able to produce geometries that better satisfy modeling requirements for cardiac flow simulations. Our source code and pretrained networks are available at https://github.com/fkong7/HeartDeformNets.