Frontiers in Physiology (Dec 2023)
A matched-pair case control study identifying hemodynamic predictors of cerebral aneurysm growth using computational fluid dynamics
Abstract
Introduction: Initiation and progression of cerebral aneurysms is known to be driven by complex interactions between biological and hemodynamic factors, but the hemodynamic mechanism which drives aneurysm growth is unclear. We employed robust modeling and computational methods, including temporal and spatial convergence studies, to study hemodynamic characteristics of cerebral aneurysms and identify differences in these characteristics between growing and stable aneurysms.Methods: Eleven pairs of growing and non-growing cerebral aneurysms, matched in both size and location, were modeled from MRA and CTA images, then simulated using computational fluid dynamics (CFD). Key hemodynamic characteristics, including wall shear stress (WSS), oscillatory shear index (OSI), and portion of the aneurysm under low shear, were evaluated. Statistical analysis was then performed using paired Wilcoxon rank sum tests.Results: The portion of the aneurysm dome under 70% of the parent artery mean wall shear stress was higher in growing aneurysms than in stable aneurysms and had the highest significance among the tested metrics (p = 0.08). Other metrics of area under low shear had similar levels of significance.Discussion: These results align with previously observed hemodynamic trends in cerebral aneurysms, indicating a promising direction for future study of low shear area and aneurysm growth. We also found that mesh resolution significantly affected simulated WSS in cerebral aneurysms. This establishes that robust computational modeling methods are necessary for high fidelity results. Together, this work demonstrates that complex hemodynamics are at play within cerebral aneurysms, and robust modeling and simulation methods are needed to further study this topic.
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