###Computational and Experimental Analysis of the Parametric Left Atrial Appendage to Assess the Risk of Thrombus Formation

Project ID: 2228bd1126 (You will need this ID for your application)

Research Theme: Healthcare Technologies

UCL Lead department: Mechanical Engineering

Department Website

Lead Supervisor: Giorgia Bosi

Project Summary:

Atrial fibrillation (AF), the most prevalent cardiac arrhythmia, affects 9% for individuals over 65 years, and it is the leading cause of thromboembolic events, such as stroke and vascular dementia. 90% of thrombi responsible for thromboembolic events during AF originate in the left atrial appendage (LAA), a protrusion of the left atrium (LA).

The project aims at analyzing different morphologies of the LA+LAA district with computational and experimental methods to perform a systematic analysis of the sensitivity of each geometrical parameter (e.g. number/dimension of lobes and trabeculae, orifice shape/dimension) influencing the fluid-dynamics, and therefore thrombus formation.

The objectives are: 1) Creating a generalised parametric Computer Aided Design (CAD) model of the LA+LAA district. 2) Developing Fluid Structure Interaction (FSI) models, imposing physiological and pathological (i.e. AF) conditions, investigating the relations between LA+LAA shape and heamodynamics with the risk of thrombus formation. 3) Manufacturing physical models with optically transparent compliant polymers mimicking LA/LAA tissue distensibility, to perform experimental tests using dyes in a specifically adapted hydro-mechanical pulse duplicator system (ViVitro Systems Inc.) to validate the computational model. The final result will be a practical classification tool supporting clinicians in the stratification of patients at risk of thrombosis, easy to immediately translate into practice.

This project is highly interdisciplinary, involving engineering experimental and computational skills, in constant contact with cardiologists and clinical morphologists, exposing the PhD student to a multidisciplinary engineering approach to tackle a very timely clinical problem. The student will take advantage of a well set-up network of collaborations among different UCL Departments (Mechanical Engineering, Institute of Cardiovascular Science and Institute of Healthcare Engineering) and Clinical Institutions (The Barts Health NHS Trust – the largest Trust in the UK – and UCLH). Applicants should ideally have experience in: • FE/CFD/FSI modelling • Experimental testing • Matlab/Python/C++ programming • Machine learning