Detailed evaluation of a population-wise personalization approach to generate synthetic myocardial infarct images

Personalization of biophysical models to real data is essential to achieve realistic simulations or generate relevant synthetic populations. However, some of these models involve randomness, which poses two challenges: they do not allow the standard personalization to each individual’s data and they lack an analytical formulation required for optimization. In previous work, we introduced a population-based personalization strategy which overcomes these challenges and demonstrated its feasibility on simple 2D geometrical models of myocardial infarct. The method consists in matching the distributions of the synthetic and real populations, quantified through the Kullback–Leibler (KL) divergence. Personalization is achieved with a gradient-free algorithm (CMA-ES), which generates sets of candidate solutions represented by their covariance matrix, whose coefficients evolve until the synthetic and real data are matched. However, the robustness of this strategy regarding settings and more complex data was not challenged. In this work, we specifically address these points, with (i) an improved design, (ii) a thorough evaluation on crucial aspects of the personalization process, including hyperparameters and initialization, and (iii) the application to 3D data. Despite some limits of the simple geometrical models used, our method is able to capture the main characteristics of the real data, as demonstrated both on 2D and 3D segmented late Gadolinium images of 123 subjects with acute myocardial infarction.