期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 42, 期 2, 页码 533-545出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2022.3219284
关键词
Geometric deep learning; mesh genera-tion; shape deformation; cardiac simulations
Patient-specific cardiac modeling using deep learning methods can efficiently generate accurate and consistent simulation-suitable models of the heart from medical images. This approach outperforms prior methods in terms of whole heart reconstruction and produces geometries that better satisfy requirements for cardiac flow simulations. The source code and pretrained networks for this method are publicly available for further development and application.
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.
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