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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Acknowledgements
The authors acknowledge the support from the French ANR (LABEX PRIMES of Univ. Lyon [ANR-11-LABX-0063] within the program “Investissements d’Avenir” [ANR-11-IDEX-0007], the JCJC project “MIC-MAC” [ANR-19-CE45-0005]), and the Fédération Francaise de Cardiologie (“MI-MIX” project, Allocation René Foudon). They are also grateful to P Croisille and M Viallon (CREATIS, CHU Saint Etienne) for providing the imaging data for the MIMI population, and M Di Folco (CREATIS) for the preliminary exploration of the data alignment and simulation tools.
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Mom, K., Clarysse, P., Duchateau, N. (2021). Population-Based Personalization of Geometric Models of Myocardial Infarction. In: Ennis, D.B., Perotti, L.E., Wang, V.Y. (eds) Functional Imaging and Modeling of the Heart. FIMH 2021. Lecture Notes in Computer Science(), vol 12738. Springer, Cham. https://doi.org/10.1007/978-3-030-78710-3_1
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