Abstract

In my talk, I will focus on two cardiac diseases: myocardial infarction and cardiomyopathy. Patient survival could be improved if clinicians could determine patient-specific tissue
properties, such as fibre stiffness and contractility, but these cannot be measured in vivo. Advances in soft-tissue mechanics now make it possible, in principle, to infer such parameters from cardiac magnetic resonance images (MRI). However, current methods are so computationally intensive, often requiring weeks on high-performance computer clusters, that clinical use is limited. In my talk, I will show how modern emulation techniques based on Gaussian processes and physics-informed graph neural networks can greatly reduce computational cost with minimal loss of accuracy,
enabling clinical decision support in a realistic time frame.