Local hemodynamic forces play an important role in determining the functional significance of coronary arterial stenosis and understanding the mechanism of coronary disease progression. Computational fluid dynamics (CFD) have been widely performed to simulate hemodynamics non-invasively from coronary computed tomography angiography (CCTA) images. However, accurate computational analysis is still limited by the complex construction of patient-specific modeling and time-consuming computation. In this work, we proposed an end-to-end deep learning framework, which could predict the coronary artery hemodynamics from CCTA images. The model was trained on the hemodynamic data obtained from 3D simulations of synthetic and real datasets. Extensive experiments demonstrated that the predicted hemdynamic distributions by our method agreed well with the CFD-derived results. Quantitatively, the proposed method has the capability of predicting the fractional flow reserve with an average error of 0.5\% and 2.5\% for the synthetic dataset and real dataset, respectively. Particularly, our method achieved much better accuracy for the real dataset compared to PointNet++ with the point cloud input. This study demonstrates the feasibility and great potential of our end-to-end deep learning method as a fast and accurate approach for hemodynamic analysis.

翻译：局部血流动力学力在判断冠状动脉狭窄的功能意义及理解冠脉疾病进展机制中发挥着重要作用。计算流体动力学（CFD）已被广泛用于从冠状动脉计算机断层扫描血管造影（CCTA）图像中无创模拟血流动力学。然而，精确的计算分析仍受到患者特异性建模复杂构建与耗时计算的双重限制。本研究提出了一种端到端深度学习框架，能够从CCTA图像直接预测冠状动脉血流动力学。该模型基于合成数据集与真实数据集的3D模拟血流动力学数据进行训练。大量实验表明，该方法预测的血流动力学分布与CFD计算结果高度吻合。在定量评估中，本方法对合成数据集和真实数据集的血流储备分数预测平均误差分别为0.5%和2.5%。值得注意的是，与采用点云输入的PointNet++相比，本方法在真实数据集上实现了显著更高的精度。该研究证实了端到端深度学习方法作为快速准确的血流动力学分析手段的可行性与巨大潜力。