Eikonal Model Personalisation using Invasive Data to Predict Cardiac Resynchronisation Therapy Electrophysiological Response

Abstract : In this manuscript, we personalise an Eikonal model of cardiac wave front propagation using data acquired during an invasive electro-physiological study. To this end, we use a genetic algorithm to determine the parameters that provide the best fit between simulated and recorded activation maps during sinus rhythm. We propose a way to parameterise the Eikonal simulations that take into account the Purkinje network and the septomarginal trabecula influences while keeping the computational cost low. We then re-use these parameters to predict the cardiac resynchronisation therapy electrophysiological response by adapting the simulation initialisation to the pacing locations. We experiment different divisions of the myocardium on which the propagation velocities have to be optimised. We conclude that separating both ventricles and both endocardia seems to provide a reasonable personalisation framework in terms of accuracy and predictive power.
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Submitted on : Monday, November 18, 2019 - 2:37:29 PM
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Nicolas Cedilnik, Maxime Sermesant. Eikonal Model Personalisation using Invasive Data to Predict Cardiac Resynchronisation Therapy Electrophysiological Response. STACOM 2019, 10th Workshop on Statistical Atlases and Computational Modelling of the Heart, Oct 2019, Shenzen, China. ⟨hal-02368288⟩

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