Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.798637
Title: A computational framework for mitral valve analysis combining multi-modality imaging, statistical shape modelling and fluid-structure simulations
Author: Biffi, Benedetta
ISNI:       0000 0004 8508 0424
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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Abstract:
In the context of cardiac disease, >300,000 people in the world are annually referred for mitral valve (MV) treatment as a consequence of MV regurgitation (MR). Current surgical strategies adopted to repair or replace the malfunctioning valve carry high risks, and considerable efforts are invested towards the development of less invasive techniques in order to reduce such risks and extend the treatment to a larger number of patients. However, the anatomy of the MV apparatus is rather complex, with several structures arranged in a non-uniform geometry interacting to guarantee the valvular function. Due to this geometrical complexity, the options for transcatheter and/or suture-less devices for MV repair or replacement on the market are limited. Given the wide variability encountered in MV anatomy and physiology, I hypothesize that patient or population- specific geometries play a relevant role in influencing the process of designing a new MV device. By exploiting clinical data acquired on relevant patient cohorts, I developed computational techniques for the automatic analysis of the MV apparatus, with the aim to provide a tool for tackling the complex problem of device optimisation via a patient or population driven approach. Specifically, I collected 3D echocardiography and cardiovascular magnetic resonance images from patients suffering from MR and who require a new valve. I processed such images with an automatic segmentation method and obtained a comprehensive virtual anatomical model of the left heart and MV. Analysed with a statistical shape modelling technique, these models allowed me to identify shape descriptors in the target patients, and to classify the full population on the basis of quantifiable anatomical 3D parameters. Finally, I used these results to generate an anatomical model of the average patient, which I combined with numerical simulations to derive mechanical and fluid-dynamics information for potential device improvements.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.798637  DOI: Not available
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