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Title: Hemodynamic data assimilation in a subject-specific circle of Willis geometry
Author(s): Gaidzik, Franziska
Pathiraja, Sahani
Saalfeld, SylviaLook up in the Integrated Authority File of the German National Library
Stucht, Daniel
Speck, OliverLook up in the Integrated Authority File of the German National Library
Thévenin, DominiqueLook up in the Integrated Authority File of the German National Library
Janiga, GáborLook up in the Integrated Authority File of the German National Library
Issue Date: 2021
Type: Article
Language: English
URN: urn:nbn:de:gbv:ma9:1-1981185920-832812
Subjects: Hemodynamics
Uncertainty quantification
Abstract: Purpose The anatomy of the circle ofWillis (CoW), the brain’s main arterial blood supply system, strongly differs between individuals, resulting in highly variable flow fields and intracranial vascularization patterns. To predict subject-specific hemodynamics with high certainty, we propose a data assimilation (DA) approach that merges fully 4D phase-contrast magnetic resonance imaging (PC-MRI) data with a numerical model in the form of computational fluid dynamics (CFD) simulations. Methods To the best of our knowledge, this study is the first to provide a transient state estimate for the three-dimensional velocity field in a subject-specific CoW geometry using DA. High-resolution velocity state estimates are obtained using the local ensemble transform Kalman filter (LETKF). Results Quantitative evaluation shows a considerable reduction (up to 90%) in the uncertainty of the velocity field state estimate after the data assimilation step. Velocity values in vessel areas that are below the resolution of the PC-MRI data (e.g., in posterior communicating arteries) are provided. Furthermore, the uncertainty of the analysis-based wall shear stress distribution is reduced by a factor of 2 for the data assimilation approach when compared to the CFD model alone. Conclusion This study demonstrates the potential of data assimilation to provide detailed information on vascular flow, and to reduce the uncertainty in such estimates by combining various sources of data in a statistically appropriate fashion.
Open Access: Open access publication
License: (CC BY-SA 4.0) Creative Commons Attribution ShareAlike 4.0(CC BY-SA 4.0) Creative Commons Attribution ShareAlike 4.0
Sponsor/Funder: Projekt DEAL 2020
Journal Title: Clinical neuroradiology
Publisher: Urban & Vogel
Publisher Place: München
Volume: 31
Issue: 3
Original Publication: 10.1007/s00062-020-00959-2
Page Start: 643
Page End: 651
Appears in Collections:Fakultät für Verfahrens- und Systemtechnik (OA)

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