Personalization of cardiac motion and contractility from images using variational data assimilation.

Personalization is a key aspect of biophysical models in order to impact clinical practice. In this paper, we propose a personalization method of electromechanical models of the heart from cine-MR images based on the adjoint method. After estimation of electrophysiological parameters, the cardiac motion is estimated based on a proactive electromechanical model.

Toward patient-specific myocardial models of the heart.

This article presents a framework for building patient-specific models of the myocardium, to help diagnosis, therapy planning, and procedure guidance. The aim is to be able to introduce such models in clinical applications. Thus, there is a need to design models that can be adjusted from clinical data, images, or signals, which are sparse and noisy.