Padhee Swati, Johnson Mark, Yi Hang, Banerjee Tanvi, Yang Zifeng
Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435, USA.
Department of Mechanical and Materials Engineering, Wright State University, Dayton, OH 45435, USA.
Bioengineering (Basel). 2022 Oct 28;9(11):622. doi: 10.3390/bioengineering9110622.
Computational fluid dynamics (CFD) is widely employed to predict hemodynamic characteristics in arterial models, while not friendly to clinical applications due to the complexity of numerical simulations. Alternatively, this work proposed a framework to estimate hemodynamics in vessels based on angiography images using machine learning (ML) algorithms. First, the iodine contrast perfusion in blood was mimicked by a flow of dye diffusing into water in the experimentally validated CFD modeling. The generated projective images from simulations imitated the counterpart of light passing through the flow field as an analogy of X-ray imaging. Thus, the CFD simulation provides both the ground truth velocity field and projective images of dye flow patterns. The rough velocity field was estimated using the optical flow method (OFM) based on 53 projective images. ML training with least absolute shrinkage, selection operator and convolutional neural network was conducted with CFD velocity data as the ground truth and OFM velocity estimation as the input. The performance of each model was evaluated based on mean absolute error and mean squared error, where all models achieved or surpassed the criteria of 3 × 10 and 5 × 10 m/s, respectively, with a standard deviation less than 1 × 10 m/s. Finally, the interpretable regression and ML models were validated with over 613 image sets. The validation results showed that the employed ML model significantly reduced the error rate from 53.5% to 2.5% on average for the v-velocity estimation in comparison with CFD. The ML framework provided an alternative pathway to support clinical diagnosis by predicting hemodynamic information with high efficiency and accuracy.
计算流体动力学（CFD）被广泛用于预测动脉模型中的血流动力学特征，但由于数值模拟的复杂性，对临床应用不太友好。作为替代方案，这项工作提出了一个框架，用于基于血管造影图像使用机器学习（ML）算法来估计血管中的血流动力学。首先，在经过实验验证的CFD建模中，通过染料扩散到水中的流动来模拟血液中的碘造影剂灌注。从模拟中生成的投影图像模仿了光线穿过流场的对应物，作为X射线成像的类比。因此，CFD模拟提供了染料流动模式的地面真值速度场和投影图像。基于53幅投影图像，使用光流法（OFM）估计粗略的速度场。以CFD速度数据作为地面真值，以OFM速度估计作为输入，进行了具有最小绝对收缩、选择算子和卷积神经网络的ML训练。基于平均绝对误差和均方误差评估每个模型的性能，所有模型分别达到或超过了3×10和5×10 m/s的标准，标准差小于1×10 m/s。最后，使用超过613个图像集对可解释回归和ML模型进行了验证。验证结果表明，与CFD相比，所采用的ML模型在v速度估计方面平均将错误率从53.5%显著降低到2.5%。ML框架通过高效准确地预测血流动力学信息，为支持临床诊断提供了一条替代途径。
