Evaluating the accuracy of cerebrovascular computational fluid dynamics modeling through time-resolved experimental validation

Scientific Reports, vol. 14, pp. 8194

Abstract

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.

Authors 6

1. Claudio A. Luisi Aachen Department of Cardiovascular Engineering Institute of Applied Medical Engineering Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Department of Cardiovascular Engineering, Institute of Applied Medical Engineering, Medical Faculty, RWTH Aachen University, Pauwelsstr. 20, 52074, Aachen, Germany

2. Tom L. Witter Aachen Department of Cardiovascular Engineering Institute of Applied Medical Engineering Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Department of Cardiovascular Engineering, Institute of Applied Medical Engineering, Medical Faculty, RWTH Aachen University, Pauwelsstr. 20, 52074, Aachen, Germany

3. Omid Nikoubashman Aachen Clinic for Diagnostic and Interventional Neuroradiology Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Clinic for Diagnostic and Interventional Neuroradiology, Medical Faculty, RWTH Aachen University, Pauwelstr. 30, 52074, Aachen, Germany

4. Martin Wiesmann Aachen Clinic for Diagnostic and Interventional Neuroradiology Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Clinic for Diagnostic and Interventional Neuroradiology, Medical Faculty, RWTH Aachen University, Pauwelstr. 30, 52074, Aachen, Germany

5. Ulrich Steinseifer Aachen Department of Cardiovascular Engineering Institute of Applied Medical Engineering Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Department of Cardiovascular Engineering, Institute of Applied Medical Engineering, Medical Faculty, RWTH Aachen University, Pauwelsstr. 20, 52074, Aachen, Germany

6. Michael Neidlin corresponding Aachen Department of Cardiovascular Engineering Institute of Applied Medical Engineering Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Department of Cardiovascular Engineering, Institute of Applied Medical Engineering, Medical Faculty, RWTH Aachen University, Pauwelsstr. 20, 52074, Aachen, Germany. neidlin@ame.rwth-aachen.de

   Department of Cardiovascular Engineering, Institute of Applied Medical Engineering, Medical Faculty, RWTH Aachen University, Pauwelsstr. 20, 52074, Aachen, Germany