MRI-based shear stress profiles in subclinical carotid atherosclerosis - A feasibility study.

Authors

Sanne C M van Kuijk, Bernhard P Berghout, Suze-Anne Korteland, Robin Y R Camarasa, Marleen de Bruijne, Daniel Bos, Ali C Akyildiz, M Kamran Ikram, Jolanda J Wentzel

Published in

PLOS digital health. Volume 5. Issue 8. Pages e0001618. Epub Aug 04, 2026.

Abstract

Arterial wall shear stress (WSS) plays an important role in atherosclerosis, but its assessment is often limited to small, retrospective studies due to technical and time constraints. This study aimed to evaluate the feasibility of WSS assessment in carotid arteries within the long-running, population-based Rotterdam Study. We developed a semi-automated computational fluid dynamics (CFD) pipeline, using deep-learning-based lumen segmentations of the carotid arteries from BlackBlood MRI and participant-specific common carotid blood flow from 3D phase-contrast MRA. Steady-state CFD simulations generated WSS profiles of the common and internal carotid arteries. Feasibility was evaluated by applying the pipeline to both carotid arteries in a sample of the Rotterdam Study, and reliability was assessed through inter- and intra-rater analyses. Simulations were successfully performed on both carotid arteries of 100 participants (mean age, 72.6 years ± 10.6 [SD]; 49 female). Automated lumen segmentations showed good agreement with manual segmentations (Dice similarity coefficient 0.89 ± 0.02). Intra- and inter-rater reliability analyses indicated good to excellent reliability of the pipeline, with intra-class correlation coefficients ranging from 0.89 to 0.98. This study demonstrates the feasibility of the CFD-based pipeline for large-scale carotid WSS assessment, integrating participant-specific carotid blood flow and automated segmentations. The reliability analyses underscore the reproducibility of the pipeline and highlight its potential for efficient, scalable carotid WSS analysis, making the pipeline suitable for application in large population-based and clinical studies.

PMID:
42550850
Bibliographic data and abstract were imported from PubMed on 05 Aug 2026.

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