Learning Whole Heart Mesh Generation From Patient Images for Computational Simulations
Learning Whole Heart Mesh Generation From Patient Images for Computational Simulations
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DOI：
10.1109/tmi.2022.3219284
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发表时间：
2022-03
影响因子：
10.6
通讯作者：
Fanwei Kong;S. Shadden
中科院分区：
文献类型：
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作者：
Fanwei Kong;S. Shadden
患者特异性心脏建模将从医学图像和生物物理模拟导出的心脏的几何形状相结合，以预测心脏功能的各个方面。然而，从患者图像数据生成适合模拟的心脏模型通常需要复杂的过程和大量的人力。我们提出了一种快速自动的深度学习方法，用于从医学图像中构建适合模拟的心脏模型。该方法通过学习使整个心脏模板上的一小组变形手柄变形来从3D患者图像构造网格。对于3D CT和MR数据，该方法实现了有前途的准确性，整个心脏重建，始终优于以前的方法，在构建模拟合适的网格的心脏。当对时间序列CT数据进行评估时，该方法产生的几何形状在解剖学和时间上比现有方法更一致，并且能够产生更好地满足心脏流动模拟的建模要求的几何形状。我们的源代码和预训练的网络可以在https://github.com/fkong7/HeartDeformNets上找到。
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.