|Authors||O. M. Khan|
|Title||Numerical characterization of high-frequency flow fluctuations in intracranial aneurysms|
|Publication Type||PhD Thesis|
|Year of Publication||2017|
|Degree awarding institution||University of Toronto|
Rupture of intracranial aneurysms (IAs), saccular outpouching of blood vessels, can lead to death or permanent disability in majority of the patients. Hemodynamic forces are thought to play a key role in the process, but, currently, can only be estimated through computational fluid dynamic (CFD) simulations of patients' cerebrovasculature. IA flows have conventionally been considered laminar and stable; however, recent studies highlighted the presence of high-frequency flow fluctuations consistent with clinical evidence Ñ a discrepancy attributed to conventional use of low-order CFD. The goal of this doctoral thesis was to investigate the nature and characteristic of these flow fluctuations, and its prevalence in IAs. Towards that goal, I performed the first mesh, time, and solver refinement study to investigate the isolated impact of each modelling choice. With 320-fold increase in mesh size, 25-fold increase in time-steps, and considering both a low-order solver and a high-order solver, I found that solver choice had a relatively higher impact on hemodynamics compared to other modelling assumptions. Finding of these flow fluctuations also meant rethinking the conventional assumption of Newtonian rheology for blood. As a first step, I performed a controlled numerical experiment on an idealized stenotic vessel for Re=500-1000, which suggested that shear-thinning non-Newtonian rheology caused a delay of 10% in the critical Reynolds number. I later investigated the practical impact of these findings on IA hemodynamics. These findings suggested that assumption of shear-thinning rheology had a negligible impact, particularly when compared to effects of low-order solvers.
Due to limitations of current hemodynamic metrics in quantifying flow fluctuations, I proposed a frequency-based hemodynamic index. Using this tool and aforementioned findings, I performed high-resolution simulations of 19 IAs to characterize and investigate the prevalence of flow fluctuations. Through proper orthogonal decomposition, I showed that, depending on the patient, a wide range of spectra exists, some with high concentration of energy in higher modes. I also found that approximately half of IA flows are "turbulent-like" and 30% can have cycle-to-cycle fluctuations. Further investigation is needed to establish the clinical impact of these flow fluctuations in IA rupture risk assessment.