Sensitivity of Hostile Hemodynamics to Intracranial Aneurysm Geometry
type: CreativeWork
description: An intracranial aneurysm (IA) is an abnormal outpouching of an artery in the brain and is thought to be present in about 1 in 30 adults. Rupture of an IA is a devastating event leading to death or disability in most cases. To supplement clinical treatment decisions, researchers have turned to hemodynamics, which are thought to contribute to the growth and rupture of IAs. Simulations using medical-image-based computational fluid dynamics (CFD) yield patient-specific risk parameters, but these parameters are often highly uncertain due to modelling limitations, limiting their clinical utility. Recent CFD studies have shown IA rupture to be associated with wall shear stress (WSS) and jet impingement, but these studies may be overlooking turbulent-like flow instabilities. This thesis investigates the nature of these flow instabilities and risk parameters in the context of uncertainty due to geometry. To characterize these instabilities, this thesis highlights the novel use of spectrograms as an interpretable summary of the flow field (Chapter 2) and demonstrates their variation in a cohort of 50 IAs (Chapter 3). Based on these spectrograms, I developed a novel marker to quantify harmonic flow features and demonstrated that this marker was a better predictor of rupture than popular WSS-based metrics in our cohort of 50 high-fidelity IA simulations (Chapter 4). Overestimation of the IA neck is a widespread issue among IA CFD researchers, which could selectively promote or inhibit the development of flow instabilities. To overcome this issue, I devel- oped a novel technique for improving IA segmentation from 3D imaging (Chapter 5) and showed that hemodynamic parameters including flow instabilities can be sensitive to these common segmentation errors, potentially muddling efforts toward rupture prediction (Chapter 6). Hemodynamic parameters are ultimately only useful if they are robust to modelling error and provide useful diagnostic information that is not more-easily extra…
abstract: An intracranial aneurysm (IA) is an abnormal outpouching of an artery in the brain and is thought to be present in about 1 in 30 adults. Rupture of an IA is a devastating event leading to death or disability in most cases. To supplement clinical treatment decisions, researchers have turned to hemodynamics, which are thought to contribute to the growth and rupture of IAs. Simulations using medical-image-based computational fluid dynamics (CFD) yield patient-specific risk parameters, but these parameters are often highly uncertain due to modelling limitations, limiting their clinical utility. Recent CFD studies have shown IA rupture to be associated with wall shear stress (WSS) and jet impingement, but these studies may be overlooking turbulent-like flow instabilities. This thesis investigates the nature of these flow instabilities and risk parameters in the context of uncertainty due to geometry. To characterize these instabilities, this thesis highlights the novel use of spectrograms as an interpretable summary of the flow field (Chapter 2) and demonstrates their variation in a cohort of 50 IAs (Chapter 3). Based on these spectrograms, I developed a novel marker to quantify harmonic flow features and demonstrated that this marker was a better predictor of rupture than popular WSS-based metrics in our cohort of 50 high-fidelity IA simulations (Chapter 4). Overestimation of the IA neck is a widespread issue among IA CFD researchers, which could selectively promote or inhibit the development of flow instabilities. To overcome this issue, I devel- oped a novel technique for improving IA segmentation from 3D imaging (Chapter 5) and showed that hemodynamic parameters including flow instabilities can be sensitive to these common segmentation errors, potentially muddling efforts toward rupture prediction (Chapter 6). Hemodynamic parameters are ultimately only useful if they are robust to modelling error and provide useful diagnostic information that is not more-easily extra…
author: MacDonald, Daniel Edward
keywords: aneurysm, cfd, fluids, geometry, imaging
date published: 2022-11
date modified: 2026-07-27T21:28:07Z
provider: University of Toronto
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Toronto

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property ID: Handle
value: http://hdl.handle.net/1807/125005