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On the Identification of Hypoxic Regions in Subject-specific Cerebral Vasculature by Combined CFD/MRI

Overview
Journal R Soc Open Sci
Specialty Science
Date 2023 Jan 13
PMID 36636311
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Abstract

A long-time exposure to lack of oxygen (hypoxia) in some regions of the cerebrovascular system is believed to be one of the causes of cerebral neurological diseases. In the present study, we show how a combination of magnetic resonance imaging (MRI) and computational fluid dynamics (CFD) can provide a non-invasive alternative for studying blood flow and transport of oxygen within the cerebral vasculature. We perform computer simulations of oxygen mass transfer in the subject-specific geometry of the circle of Willis. The computational domain and boundary conditions are based on four-dimensional (4D)-flow MRI measurements. Two different oxygen mass transfer models are considered: passive (where oxygen is treated as a dilute chemical species in plasma) and active (where oxygen is bonded to haemoglobin) models. We show that neglecting haemoglobin transport results in a significant underestimation of the arterial wall mass transfer of oxygen. We identified the hypoxic regions along the arterial walls by introducing the critical thresholds that are obtained by comparison of the estimated range of Damköhler number ( ⊂ 〈9; 57〉) with the local Sherwood number. Finally, we recommend additional validations of the combined MRI/CFD approach proposed here for larger groups of subject- or patient-specific brain vasculature systems.

Citing Articles

Numerical Modeling of Flow in the Cerebral Vasculature: Understanding Changes in Collateral Flow Directions in the Circle of Willis for a Cohort of Vasospasm Patients Through Image-Based Computational Fluid Dynamics.

Straccia A, Barbour M, Chassagne F, Bass D, Barros G, Leotta D Ann Biomed Eng. 2024; 52(9):2417-2439.

PMID: 38758460 PMC: 11329356. DOI: 10.1007/s10439-024-03533-w.


On the identification of hypoxic regions in subject-specific cerebral vasculature by combined CFD/MRI.

Perinajova R, van Ooij P, Kenjeres S R Soc Open Sci. 2023; 10(1):220645.

PMID: 36636311 PMC: 9810418. DOI: 10.1098/rsos.220645.

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