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Personalised haemodynamic simulations of aortic dissection: towards clinical translation

Bonfanti, Mirko; (2019) Personalised haemodynamic simulations of aortic dissection: towards clinical translation. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Abstract

Aortic dissection (AD) is a severe vascular condition in which an intramural tear results in blood flowing within the aortic wall. The optimal treatment of type-B dissections - those involving the arch and descending aorta - is still debated; when uncomplicated, they are commonly managed medically, but up to 50% of the cases will develop complications requiring invasive intervention. Patient-specific computational fluid dynamics (CFD) can provide insight into the pathology and aid clinical decisions by reproducing in detail the intra-aortic haemodynamics; however, oversimplified modelling assumptions and high computational cost compromise the accuracy of simulation predictions and impede clinical translation. Moreover, the requirement of working with noisy and oftentimes minimal clinical datasets complicates the implementation of personalised models. In the present thesis, methods to overcome the aforementioned limitations and facilitate the clinical translation of CFD tools are presented and tested on type-B AD cases. A novel approach for patient-specific models of complex ADs informed by commonly available clinical datasets (including CT-scans and Doppler ultrasonography) is proposed. The approach includes an innovative way to account for arterial compliance in rigid-wall simulations using a lumped capacitor and a parameter estimation strategy for Windkessel boundary conditions. The approach was tested on three case-studies, and the results were successfully compared against invasive intra-aortic pressure measurements. A new and efficient moving boundary method (MBM) - tunable with non-invasive displacement data - is then proposed to capture wall motion in CFD simulations, necessary in certain AD settings for accurate haemodynamic predictions. The MBM was first applied and validated on a case-study previously investigated with a full fluid-structure interaction technique, and then employed in a patient-specific compliant model of a type-B AD informed by multi-modal imaging data. Extensive comparison between in silico and in vivo data demonstrated the reliability of the model predictions.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Personalised haemodynamic simulations of aortic dissection: towards clinical translation
Event: UCL (University College London)
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2019. 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.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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 Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10087861
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