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.

翻译：计算流体动力学（CFD）可用于评估血流动力学，但其常规应用受限于劳动密集型的手动分割、CFD网格生成以及耗时的仿真流程。本研究旨在训练一个深度学习模型，使其既能从3D心脏MRI数据生成患者特异性的肺动脉体积网格，又能直接估算CFD流场。本研究使用了来自公开数据集和私有数据集的135例3D心脏MRI影像，对其中肺动脉进行手动分割并转换为体积网格。基于真实网格开展CFD仿真，并将结果插值到点-点对应网格以构建真实数据集。该数据集按85/10/15的比例划分为训练集、验证集和测试集。我们训练了一种名为Image2Flow的混合图像与图卷积神经网络，该网络能够将肺动脉模板转化为患者特异性解剖结构及CFD值。通过逐节点比较评估Image2Flow的分割性能及CFD预测精度，并开展了Image2Flow与基于机器学习分割的CFD仿真的中心线对比分析。Image2Flow取得了优异的分割精度，Dice系数中位数为0.9（四分位距：0.86-0.92）。压力和速度幅值的节点归一化绝对误差中位数分别为11.98%（四分位距：9.44-17.90%）和8.06%（四分位距：7.54-10.41%）。中心线分析显示，Image2Flow与基于机器学习生成体积网格的传统CFD仿真结果之间无显著差异。这项概念验证研究表明，同时实现基于患者特异性体积网格的分割与压力/流场估算是可行的。Image2Flow在约205毫秒内完成分割与CFD计算，比手动方法快约7000倍，使其在临床环境中更具可行性。