Department of Cardiovascular Engineering, Institute of Applied Medical Engineering, Medical Faculty, RWTH Aachen University, Pauwelsstr. 20, 52074, Aachen, Germany.
Clinic for Diagnostic and Interventional Neuroradiology, Medical Faculty, RWTH Aachen University, Pauwelstr. 30, 52074, Aachen, Germany.
Sci Rep. 2024 Apr 8;14(1):8194. doi: 10.1038/s41598-024-58925-8.
Accurate modeling of cerebral hemodynamics is crucial for better understanding the hemodynamics of stroke, for which computational fluid dynamics (CFD) modeling is a viable tool to obtain information. However, a comprehensive study on the accuracy of cerebrovascular CFD models including both transient arterial pressures and flows does not exist. This study systematically assessed the accuracy of different outlet boundary conditions (BCs) comparing CFD modeling and an in-vitro experiment. The experimental setup consisted of an anatomical cerebrovascular phantom and high-resolution flow and pressure data acquisition. The CFD model of the same cerebrovascular geometry comprised five sets of stationary and transient BCs including established techniques and a novel BC, the phase modulation approach. The experiment produced physiological hemodynamics consistent with reported clinical results for total cerebral blood flow, inlet pressure, flow distribution, and flow pulsatility indices (PI). The in-silico model instead yielded time-dependent deviations between 19-66% for flows and 6-26% for pressures. For cerebrovascular CFD modeling, it is recommended to avoid stationary outlet pressure BCs, which caused the highest deviations. The Windkessel and the phase modulation BCs provided realistic flow PI values and cerebrovascular pressures, respectively. However, this study shows that the accuracy of current cerebrovascular CFD models is limited.
准确的脑血流动力学建模对于更好地理解中风的血流动力学至关重要，计算流体动力学（CFD）建模是获取信息的可行工具。然而，目前还没有关于包括瞬态动脉压和流量在内的脑血管 CFD 模型的准确性的综合研究。本研究通过 CFD 建模与体外实验比较，系统评估了不同出口边界条件（BC）的准确性。实验装置由解剖脑血管模型和高分辨率流量和压力数据采集组成。相同脑血管几何形状的 CFD 模型包括五组稳态和瞬态 BC，包括已建立的技术和一种新的 BC，即相位调制方法。该实验产生的生理血流动力学与总脑血流量、入口压力、血流分布和血流脉动指数（PI）的报告临床结果一致。而在计算机模型中，流量的时间依赖性偏差在 19-66%之间，压力的时间依赖性偏差在 6-26%之间。对于脑血管 CFD 建模，建议避免使用会导致最大偏差的静态出口压力 BC。脉流和相位调制 BC 分别提供了真实的流量 PI 值和脑血管压力。然而，本研究表明，目前脑血管 CFD 模型的准确性有限。
