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Title: Personalised haemodynamic simulations of aortic dissection : towards clinical translation
Author: Bonfanti, Mirko
ISNI:       0000 0004 8508 1486
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available