ArticleScientific reports2024
Evaluating the accuracy of cerebrovascular computational fluid dynamics modeling through time-resolved experimental validation.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Computational Modeling of Flow in an in Vitro Cerebrovascular Model Under Pulsatile Conditions with Experimental Validation.Cardiovascular engineering and technology · 2026Article
- Personalized Computational Fluid Dynamics Analysis of Cerebral Venous Hemodynamics in a Case of Deep Cerebral Vein Thrombosis.Journal of personalized medicine · 2025Article
- Harnessing Nonlinear Mechanics to Transform Medical Diagnostics.Applied mechanics reviews · 2025Article
- Maximum admittance method for cerebrovascular outlet boundary conditions and importance of stenosis severity as a dominant factor on hemodynamics.Scientific reports · 2025Article
- Collateral flow and pulsatility during large vessel occlusions: insights from a quantitativeFrontiers in bioengineering and biotechnology · 2024Article
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6 authors.
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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.
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