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2021 | OriginalPaper | Chapter

Population-Based Personalization of Geometric Models of Myocardial Infarction

Authors : Kannara Mom, Patrick Clarysse, Nicolas Duchateau

Published in: Functional Imaging and Modeling of the Heart

Publisher: Springer International Publishing

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Abstract

We propose a strategy to perform population-based personalization of a model, to overcome the limits of case-based personalization for generating virtual populations from models that include randomness. We formulate the problem as matching the synthetic and real populations by minimizing the Kullback-Leibler divergence between their distributions. As an analytical formulation of the models is complex or even impossible, the personalization is addressed by a gradient-free method: the CMA-ES algorithm, whose relevance was demonstrated for the case-based personalization of complex biomechanical cardiac models. The algorithm iteratively adapts the covariance matrix which in our problem encodes the distribution of the synthetic data.

We demonstrate the feasibility of this approach on two simple geometrical models of myocardial infarction, in 2D, to better focus on the relevance of the personalization process. Our strategy is able to reproduce the distribution of 2D myocardial infarcts from the segmented late Gadolinium images of 123 subjects with acute myocardial infarction.

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Title: Population-Based Personalization of Geometric Models of Myocardial Infarction
Authors: Kannara Mom
Patrick Clarysse
Nicolas Duchateau
Publisher: Springer International Publishing
Book: Functional Imaging and Modeling of the Heart
Print ISBN: 978-3-030-78709-7

Electronic ISBN: 978-3-030-78710-3

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-78710-3_1

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