论文标题
深层神经网络，用于快速获取主动脉3D压力和速度流场
Deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields
论文作者
论文摘要
计算流体动力学（CFD）可用于模拟血管血流动力学并分析潜在的治疗方案。 CFD已证明对改善患者预后是有益的。但是，尚未实现CFD的实施CFD。 CFD的障碍包括高计算资源，设计模拟设置所需的专业经验以及较长的处理时间。这项研究的目的是探索使用机器学习（ML）以自动和快速回归模型复制常规主动脉CFD。用于训练/测试的数据该模型由在合成生成的3D主动脉形状上执行的3,000个CFD模拟组成。这些受试者是由基于实际患者特异性主动脉（n = 67）的统计形状模型（SSM）产生的。对200个测试形状进行的推理导致压力和速度的平均误差分别为6.01％+/- 3.12 SD和3.99％+/- 0.93 SD。我们的基于ML的型号以+/- 0.075秒（比求解器快4,000倍）执行CFD。这项概念验证的研究表明，可以在自动过程中以更快的速度且准确性高得出的ML来复制常规血管CFD的结果。
Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N=67). Inference performed on 200 test shapes resulted in average errors of 6.01% +/-3.12 SD and 3.99% +/-0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in +/-0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with high accuracy.
