Prof B Keavney, Dr A Keshmiri
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
About the Project
Congenital Heart Disease (CHD) is a heart condition resulting from an abnormality in heart structure or function that is present at birth. In the UK alone, there are about 4,600 babies born with congenital heart disease each year. Computational Fluid Dynamics (CFD), or in silico modelling, has had a profound impact on cardiovascular medicine in the past decade [2] and will be used in this PhD project to assess its potential as an assistive tool in diagnosis and treatment of two CHD conditions, namely Tetralogy of Fallot (ToF) and Coarctation of Aorta (CoA). This interdisciplinary project will also identify haemodynamic features that correlate with need for re-operation in the case of ToF and as a tool to predict hypertension in CoA. The proposed PhD research will involve 4 phases:
Phase1: Structural and flow data will be extracted from CT, echo and serial MR scans in 8-10 child patients between the ages of 8 and 13 years with either repaired ToF or repaired CoA. The age range has been selected as it is during these years that rapid deterioration of RV function or increases in blood pressure can particularly occur, in a timeframe congruent with the proposal duration.
Phase2: CFD analysis will be undertaken using available imaging data to determine haemodynamic influence [3] on post-surgical morphological recovery. Features that might correlate with need for re-operation, which the models will quantify, include excessive haemodynamic load on tissue (leading to fibrosis), excessive loads on valves (leading to degeneration and/or structural deterioration), and thrombosis on valves (risk of which can be assessed using standard flow derived metrics). Following an initial validation of the methodology, to ensure best practice and reasonable mitigation of error in geometry capture, domain discretization and inlet flow prescription for CFD studies will be performed on the complete set of baseline image sets using both non-pulsatile and pulsatile flow with fixed and dynamic representation of the pulmonary valve respectively similar to the work done by the supervisory team in [5-6].
Phase3: A detailed morphological characterisation of the geometry from extracted 4D image sets will be performed to identify post-repair evolution in volume, sites of wall-remodelling, fibrosis, valve-annulus dimension, degree of regurgitation, etc. This characterisation will be parameterised and compared with CFD results from the Phase2 to identify direct causation [7].
Phase4: Additional data from the registry such as genetic characterisation and modifiable/non-modifiable risk factors will be incorporated into a data-mining framework based on Bayesian Gaussian modelling [8]. CFD models in TOF and Coarctation will be built to generate pilot data regarding the potential of the models to predict haemodynamic deterioration using the longitudinal data collected during this project.
Funding Notes
This is a fully-funded studentship through the EPSRC DTP for 3.5 years, commencing September 2017. Applicants must be from the UK/EU and have obtained (or be about to obtain) a minimum 2:1 Bachelors degree in a relevant subject area. Applications should be submitted online, select PhD Biomedical Engineering on the application form. Interviews will be held in Manchester in May 2017.
References
References: [6] McElroy, M., Ruiz-Soler, A. & Keshmiri, A. (2016), J. Procedia CIRP, 49: 163-169. [7] Jansen IGH, et al. (2014) American Journal of Neuroradiology 35(8):1543-1548. [8] Kolachalama VB, Bressloff NW, & Nair PB (2007) Biomed. Eng. Online 6:15.