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Geometrically induced wall shear stress variability in CFD-MRI coupled simulations of blood flow in the thoracic aortas.

Authors :
Perinajová R
Juffermans JF
Westenberg JJM
van der Palen RLF
van den Boogaard PJ
Lamb HJ
Kenjereš S
Source :
Computers in biology and medicine [Comput Biol Med] 2021 Jun; Vol. 133, pp. 104385. Date of Electronic Publication: 2021 Apr 20.
Publication Year :
2021

Abstract

Aortic aneurysm is associated with aberrant blood flow and wall shear stress (WSS). This can be studied by coupling magnetic resonance imaging (MRI) with computational fluid dynamics (CFD). For patient-specific simulations, extra attention should be given to the variation in segmentation of the MRI data-set and its effect on WSS. We performed CFD simulations of blood flow in the aorta for ten different volunteers and provided corresponding WSS distributions. The aorta of each volunteer was segmented four times. The same inlet and outlet boundary conditions were applied for all segmentation variations of each volunteer. Steady-state CFD simulations were performed with inlet flow based on phase-contrast MRI during peak systole. We show that the commonly used comparison of mean and maximal values of WSS, based on CFD in the different segments of the thoracic aorta, yields good to excellent correlation (0.78-0.95) for rescan and moderate to excellent correlation (0.64-1.00) for intra- and interobserver reproducibility. However, the effect of geometrical variations is higher for the voxel-to-voxel comparison of WSS. With this analysis method, the correlation for different segments of the whole aorta is poor to moderate (0.43-0.66) for rescan and poor to good (0.48-0.73) for intra- and interobserver reproducibility. Therefore, we advise being critical about the CFD results based on the MRI segmentations to avoid possible misinterpretation. While the global values of WSS are similar for different modalities, the variation of results is high when considering the local distributions.<br /> (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Details

Language :
English
ISSN :
1879-0534
Volume :
133
Database :
MEDLINE
Journal :
Computers in biology and medicine
Publication Type :
Academic Journal
Accession number :
33894502
Full Text :
https://doi.org/10.1016/j.compbiomed.2021.104385