Longitudinal Analysis using Personalised 3D Cardiac Models with Population-Based Priors: Application to Paediatric Cardiomyopathies

Abstract : Personalised 3D modelling of the heart is of increasing interest in order to better characterise pathologies and predict evolution. The personalisation consists in estimating the parameter values of an electromechanical model in order to reproduce the observed cardiac motion. However, the number of parameters in these models can be high and their estimation may not be unique. This variability can be an obstacle to further analyse the estimated parameters and for their clinical interpretation. In this paper we present a method to perform consistent estimations of electromechanical parameters with prior probabilities on the estimated values, which we apply on a large database of 84 different heartbeats. We show that the use of priors reduces considerably the variance in the estimated parameters, enabling better conditioning of the parameters for further analysis of the cardiac function. This is demonstrated by the application to longitudinal data of paediatric cardiomyopathies, where the estimated parameters provide additional information on the pathology and its evolution.
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Submitted on : Thursday, July 27, 2017 - 2:43:25 PM
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Roch Molléro, Hervé Delingette, Manasi Datar, Tobias Heimann, Jakob Hauser, et al.. Longitudinal Analysis using Personalised 3D Cardiac Models with Population-Based Priors: Application to Paediatric Cardiomyopathies. Medical Image Computing and Computer Assisted Intervention (MICCAI), Sep 2017, Québec City, Canada. pp.350-358, ⟨10.1007/978-3-319-66185-8_40⟩. ⟨hal-01569735⟩

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