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Abstract-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. Then cardiac contractilities on two or three regions are estimated by minimizing the discrepancy between measured and simulation motion. Evaluation of the method on three patients with infarcted or dilated myocardium is provided.

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Abstract. We present a method for cardiac motion recovery using the adjustment of an electromechanical model of the heart to cine MRI. This approach is based on a proactive model which consists in a constrained minimisation of an energy coupling the model and the data. The presented method relies on specific image features in order to constrain the motion of the endocardia and epicardium and impose boundary conditions at the base. Thus, image intensity and gradient information are used to constrain the motion of the myocardium surfaces while a 3D block matching technique leads to the motion estimation of base vertices. Finally, we show that the implicit time integration of those forces and personalised boundary conditions lead to a better cardiac motion recovery from cine-MR images.

Abstract. Cardiac resynchronisation therapy (CRT) has been shown to be an effective adjunctive treatment for patients with dyssynchronous ventricular contraction and symptoms of the heart failure. However, clinical trials have also demonstrated that up to 30% of patients may be classified as non-responders. In this article, we present how the personalisation of an electromechanical model of the myocardium could help the therapy planning for CRT. We describe the four main components of our myocardial model, namely the anatomy, the electrophysiology, the kinematics and the mechanics. For each of these components we combine prior knowledge and observable parameters in order to personalise these models to patient data. Then the acute effects of a pacemaker on the cardiac function are predicted with the in silico model on a clinical case. This is a proof of concept of the potential of virtual physiological models to better select and plan the therapy.