This project focusses on the uncertainty in performance of a shape memory alloy, self-expanding transcatheter replacement valve when deployed in diseased aortic heart valves. The aortic heart valve controls the flow of blood out of the heart and into the aorta, the large blood vessel that supplies blood around the body. It will involve computational methods of robust design applied to the results obtained from simulations of device deployment (in patient-specific aortic root models) obtained from finite element analysis. As life expectancy increases, the prevalence of valvular heart disease represents a serious and growing public health problem. For people over the age of 75, approximately one in eight have some form of valvular disease including aortic stenosis (AS), typified by a build-up of hard deposits that prevent the leaflets from fully opening. Treatment for AS was revolutionised in 2002 with the first transcatheter aortic valve implantation (TAVI). Relative to open heart surgical valve replacement, TAVI is far less traumatic for the patient involving shorter hospital stays and greater cost-effectiveness. With projected double digit growth in the TAVI market over the next five to ten years, there will be a heightened need for increased device reliability and longevity. However, these requirements are compromised by patient variability in diseased valve anatomy leading to uncertainty with device deployment. Typically, a deployed TAVI device will be distorted in a way that is dependent on the orientation of deployment and the prosthetic leaflets will fail to perform as designed, especially in the presence of hard deposits.
Against this background, the main aims of this project are to (i) assess the behaviour of prosthetic leaflets following sub-optimal device deployment of a shape memory alloy, self-expanding TAVI device and (ii) seek improved device designs that are more robust to the uncertainties associated with the resulting device distortion. Advanced computational methods will be used including image segmentation, geometry construction, finite element analysis and design optimisation under uncertainty. Using real patient cases from the TAVI centre in the University Hospital Southampton Trust, robust designs will be sought in a trade-off between the mean and variance of relevant leaflet performance metrics such as mean and peak stresses. Suitable candidates will have experience in computational engineering and design including optimisation and/or finite element analysis.
If you wish to discuss any details of the project informally, please contact Prof Neil W. Bressloff in the Computational Engineering & Design research group, Email: [email protected], Tel: +44 (0) 2380 59 5473.