Fast personalization of cardiac mechanical models using parametric physics informed neural networks

We propose a parametric physics-informed neural network that can be personalized to left-ventricular anatomies from cardiac MRI data. The model combines a left-ventricular anatomical shape model derived from cardiac MRI data, and a functional model derived from synthetic cardiac deformations. The network is trained with a label-free approach using a physics-based cost-function in less than 5 minutes on a single CPU. Network inputs are endocardium pressure and myocyte activation. A complete cardiac cycle can be simulated in less than a minute. This approach is 30 times faster than the corresponding finite element simulation even when including training time.