Stokes, Catriona Lâle;
(2023)
Image-Based Computational Haemodynamics in Healthy and Dissected Aortae: Enhancing Patient-Specific Workflows using 4D-Flow MRI.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Aortic Dissection (AD) is a cardiovascular disease in which a tear develops in the intimal layer of the aorta. Pressurised blood delaminates the intimal and medial layers, separating the true lumen from a false lumen with lifethreatening consequences. The mechanisms of AD pathogenesis and progression remain poorly understood, and existing predictive metrics perform inadequately; there is a need for higher-quality information to support treatment planning and risk stratification in the clinic. Haemodynamic features such as pressure and wall shear stress are heavily implicated in the pathophysiology of AD, so their analysis represents a growing sector of AD-related research. 4D-Flow Magnetic Resonance Imaging (4DMR) and Computational Fluid Dynamics (CFD) are the two favoured haemodynamic analysis modalities. 4DMR is a medical imaging technique which measures blood velocity distributions in vivo, but with greatly restricted spatiotemporal resolution. CFD offers sufficient resolution, but its accuracy is dictated by an array of modelling assumptions for which no stablished best practice exists. Combined, these two modalities produce data with higher resolution and accuracy than in isolation. In this thesis, 4DMR data is integrated into patient-specific simulation workflows in three novel ways to enhance and understand simulation accuracy. A fully MRI-based, efficient compliance modelling technique is first developed from an existing method to preserve patient-specific accuracy whilst minimising input data requirements. Next, the impact of various patient-specific inlet conditions is explored in an AD patient, indicating that their effects are more widespread than previously thought. Finally, a number of widely-neglected minor aortic branches are included in a patient-specific AD simulation for the first time, where their inclusion facilitates improved agreement with in vivo data. By offering novel insights into the effects of widely-employed modelling assumptions and providing techniques to limit their impact on accuracy, this work advances CFD techniques toward clinical application.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Image-Based Computational Haemodynamics in Healthy and Dissected Aortae: Enhancing Patient-Specific Workflows using 4D-Flow MRI |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2023. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| Keywords: | Aortic Dissection, Aortic Haemodynamics, Cardiovascular Engineering, Computational Fluid Dynamics |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10169964 |
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