Computational fluid dynamics (CFD) can be used for evaluation of
hemodynamics. However, its routine use is limited by labor-intensive manual
segmentation, CFD mesh creation, and time-consuming simulation. This study aims
to train a deep learning model to both generate patient-specific volume-meshes
of the pulmonary artery from 3D cardiac MRI data and directly estimate CFD flow
fields.
This study used 135 3D cardiac MRIs from both a public and private dataset.
The pulmonary arteries in the MRIs were manually segmented and converted into
volume-meshes. CFD simulations were performed on ground truth meshes and
interpolated onto point-point correspondent meshes to create the ground truth
dataset. The dataset was split 85/10/15 for training, validation and testing.
Image2Flow, a hybrid image and graph convolutional neural network, was trained
to transform a pulmonary artery template to patient-specific anatomy and CFD
values. Image2Flow was evaluated in terms of segmentation and accuracy of CFD
predicted was assessed using node-wise comparisons. Centerline comparisons of
Image2Flow and CFD simulations performed using machine learning segmentation
were also performed.
Image2Flow achieved excellent segmentation accuracy with a median Dice score
of 0.9 (IQR: 0.86-0.92). The median node-wise normalized absolute error for
pressure and velocity magnitude was 11.98% (IQR: 9.44-17.90%) and 8.06% (IQR:
7.54-10.41), respectively. Centerline analysis showed no significant difference
between the Image2Flow and conventional CFD simulated on machine
learning-generated volume-meshes.
This proof-of-concept study has shown it is possible to simultaneously
perform patient specific volume-mesh based segmentation and pressure and flow
field estimation. Image2Flow completes segmentation and CFD in ~205ms, which
~7000 times faster than manual methods, making it more feasible in a clinical
environment.

本研究使用深度学习模型生成患者特异性体积网格和直接估计血流动力学数据，在简化博弈过程的同时，提供更高效的计算流体力学模拟和分割方法。

Image2Flow：一种用于快速患者特定肺动脉分割和从 3D 心脏 MRI 数据计算 CFD 流场的混合图像和图卷积神经网络

Image2Flow: A hybrid image and graph convolutional neural network for  rapid patient-specific pulmonary artery segmentation and CFD flow field  calculation from 3D cardiac MRI data