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PMID: 38589554 Published · epublish English

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

Scientific reports ·Vol. 14 ·No. 1 ·2024-00-08

Luisi CA, Witter TL, Nikoubashman O, Wiesmann M, Steinseifer U, Neidlin M

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.

MeSH 主题词
Blood Flow Velocity Hydrodynamics Hemodynamics Arterial Pressure Computer Simulation Cerebrovascular Circulation Models, Cardiovascular
Article Info
Journal
Scientific reports
Abbr.
Sci Rep
ISSN
2045-2322
Corresponding email
Published
2024-00-08
Language
English
Country/Region
England
NLM ID
101563288
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