Personalization of a cardiac electromechanical model using reduced order unscented Kalman filtering from regional volumes

• Full personalization strategy of the Bestel–Clément–Sorine electromechanical model of the heart.
• Global parameter estimation from volumes using the Unscented Transform.
• Local contractilities estimation from regional volumes using the Reduced Order Unscented Kalman Filter.
• Personalization successfully tested on 8 volunteers and 3 pathological cases.
• Preliminary specificity study in agreement with medical experiment.

Patient-specific cardiac modeling can help in understanding pathophysiology and therapy planning. However it requires to combine functional and anatomical data in order to build accurate models and to personalize the model geometry, kinematics, electrophysiology and mechanics. Personalizing the electromechanical coupling from medical images is a challenging task. We use the Bestel–Clément–Sorine (BCS) electromechanical model of the heart, which provides reasonable accuracy with a reasonable number of parameters (14 for each ventricle) compared to the available clinical data at the organ level. We propose a personalization strategy from cine MRI data in two steps. We first estimate global parameters with an automatic calibration algorithm based on the Unscented Transform which allows to initialize the parameters while matching the volume and pressure curves. In a second step we locally personalize the contractilities of all AHA (American Heart Association) zones of the left ventricle using the reduced order unscented Kalman filtering on Regional Volumes. This personalization strategy was validated synthetically and tested successfully on eight healthy and three pathological cases.

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