Purpose- Coarctation of the Aorta (CoA) patient-specific computational fluid dynamics (CFD) studies in resource constrained settings are limited by the available imaging modalities for geometry and velocity data acquisition. Doppler echocardiography has been seen as a suitable velocity acquisition modality due to its higher availability and safety. This study aimed to investigate the application of classical machine learning (ML) methods to create an adequate and robust approach for obtaining boundary conditions (BCs) from Doppler Echocardiography images, for haemodynamic modeling using CFD. Methods- Our proposed approach combines ML and CFD to model haemodynamic flow within the region of interest. With the key feature of the approach being the use of ML models to calibrate the inlet and outlet boundary conditions (BCs) of the CFD model. The key input variable for the ML model was the patients heart rate as this was the parameter that varied in time across the measured vessels within the study. ANSYS Fluent was used for the CFD component of the study whilst the scikit-learn python library was used for the ML component. Results- We validated our approach against a real clinical case of severe CoA before intervention. The maximum coarctation velocity of our simulations were compared to the measured maximum coarctation velocity obtained from the patient whose geometry is used within the study. Of the 5 ML models used to obtain BCs the top model was within 5\% of the measured maximum coarctation velocity. Conclusion- The framework demonstrated that it was capable of taking variations of the patients heart rate between measurements into account. Thus, enabling the calculation of BCs that were physiologically realistic when the heart rate was scaled across each vessel whilst providing a reasonably accurate solution.
翻译:Aorta (CoA) 患者专用计算流体动态(CFD) 在资源受限环境下的计算流动态(CFD) 研究的用途加固(CFD) 受现有几何和速度数据采集成像模式的限制。多普勒回声心电图因其可用性和安全性较高而被视为一种合适的速度获取模式。这项研究旨在调查古典机器学习(ML)方法的应用,以创造适当和稳健的方法,从多普勒耳耳心电图中获取边界条件(BCs),用CFD进行血液流动模型。我们拟议的方法结合了ML和CFD在感兴趣的区域内模拟血液动力流动模式。使用ML模型的主要特征是使用ML模型校准内最大心脏流动数据模型进行最大速度的计算。我们测量的卡路里最大心脏流数据流数据流数据流数据流流数据流数据流数据流数据流数据流数据流中,我们用于对卡路里最大速度数据流数据流数据流数据流数据流数据流进行最精确的计算。