  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.
