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Title: Differential sensitivities to blood pressure variations in internal carotid and intracranial arteries: a numerical approach to stroke prediction.
Austin Authors: Kizhisseri, Muhsin;Gharaie, Saleh;Boopathy, Sethu Raman;Lim, Ruth P ;Mohammadzadeh, Milad;Schluter, Jorg
Affiliation: School of Engineering, Deakin University, 75 Pigdons Rd, Waurn Ponds, VIC, 3216, Australia.
See-Mode Technologies Pte Ltd, Melbourne, Australia.
Austin Health
See-Mode Technologies Pte Ltd, Melbourne, Australia.
Issue Date: 15-Dec-2023
Date: 2023
Publication information: Scientific Reports 2023-12-15; 13(1)
Abstract: Stroke remains a global health concern, necessitating early prediction for effective management. Atherosclerosis-induced internal carotid and intra cranial stenosis contributes significantly to stroke risk. This study explores the relationship between blood pressure and stroke prediction, focusing on internal carotid artery (ICA) branches: middle cerebral artery (MCA), anterior cerebral artery (ACA), and their role in hemodynamics. Computational fluid dynamics (CFD) informed by the Windkessel model were employed to simulate patient-specific ICA models with introduced stenosis. Central to our investigation is the impact of stenosis on blood pressure, flow velocity, and flow rate across these branches, incorporating Fractional Flow Reserve (FFR) analysis. Results highlight differential sensitivities to blood pressure variations, with M1 branch showing high sensitivity, ACA moderate, and M2 minimal. Comparing blood pressure fluctuations between ICA and MCA revealed heightened sensitivity to potential reverse flow compared to ICA and ACA comparisons, emphasizing MCA's role. Blood flow adjustments due to stenosis demonstrated intricate compensatory mechanisms. FFR emerged as a robust predictor of stenosis severity, particularly in the M2 branch. In conclusion, this study provides comprehensive insights into hemodynamic complexities within major intracranial arteries, elucidating the significance of blood pressure variations, flow attributes, and FFR in stenosis contexts. Subject-specific data integration enhances model reliability, aiding stroke risk assessment and advancing cerebrovascular disease understanding.
DOI: 10.1038/s41598-023-49591-3
Journal: Scientific Reports
Start page: 22319
PubMed URL: 38102319
ISSN: 2045-2322
Type: Journal Article
Subjects: Stroke/diagnosis
Cerebrovascular Circulation/physiology
Appears in Collections:Journal articles

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