- Academic lead
- Dr Erica Dall’Armellina, School of Medicine, E.DallArmellina@leeds.ac.uk
- Prof Peter Jimack, School of Computing, firstname.lastname@example.org, Dr Toni Lassila, School of Computing, T.Lassila@leeds.ac.uk
- Project themes
- Biomedical Flows
Heart failure post myocardial infarction is a worldwide massive burden with an anticipated prevalence increase of 46% before 2030. The main cause of heart failure is the change in LV shape (i.e. remodelling) due to damaged muscle causing an inefficiency of the pumping action. Very little is known on the underpinning mechanisms of LV remodelling and novel tools are needed to address therapy. Cardiac magnetic resonance offers superb novel techniques to accurately quantify changes in myocardial architecture (by diffusion tensor imaging, see figure) in relation to changes in intracavitary flow dynamics (by 4D flow, see figure). This project is a unique opportunity to develop computational models of combined intracavitary fluid dynamics and myocardial structural changes to predict changes in the function of the heart following a heart attack. These models will combine state-of-the-art neural network models for fluid dynamics with patient-specific tissue mechanics models based on patient-specific flow and tissue imaging.