Numerical analysis methods including finite element analysis (FEA), computational fluid dynamics (CFD), and fluid-structure interaction (FSI) analysis have been used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. However, for patient-specific computational analysis, complex procedures are usually required to set-up the models, and long computing time is needed to perform the simulation, preventing fast feedback to clinicians in time-sensitive clinical applications. In this study, by using machine learning techniques, we developed deep neural networks (DNNs) to directly estimate the steady-state distributions of pressure and flow velocity inside the thoracic aorta. After training on hemodynamic data from CFD simulations, the DNNs take as input a shape of the aorta and directly output the hemodynamic distributions in one second. The trained DNNs are capable of predicting the velocity magnitude field with an average error of 1.9608% and the pressure field with an average error of 1.4269%. This study demonstrates the feasibility and great potential of using DNNs as a fast and accurate surrogate model for hemodynamic analysis of large blood vessels.

译文

包括有限元分析 (FEA),计算流体动力学 (CFD) 和流体-结构相互作用 (FSI) 分析在内的数值分析方法已用于研究人体组织和器官以及组织-医疗设备的生物力学相互作用和治疗策略。但是,对于特定于患者的计算分析,通常需要复杂的程序来建立模型,并且需要较长的计算时间来执行模拟,从而阻止了对时间敏感的临床应用中的临床医生的快速反馈。在这项研究中,通过使用机器学习技术,我们开发了深度神经网络 (dnn) 来直接估计胸主动脉内部压力和流速的稳态分布。在对来自CFD模拟的血液动力学数据进行训练之后,dnn将主动脉的形状作为输入,并在一秒钟内直接输出血液动力学分布。训练的dnn能够预测平均误差为1.9608% 的速度幅度场和平均误差为1.4269% 的压力场。这项研究证明了使用DNNs作为大血管血流动力学分析的快速准确替代模型的可行性和巨大潜力。

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