Domain Adaptation via Dimensionality Reduction for the Comparison of Cardiac Simulation Models

Abstract

We tackle the determination of a relevant data space to quantify differences between two databases coming from different sources. In the present paper, we propose to quantify differences between cardiac simulations from two different biomechanical models, assessed through myocardial deformation patterns. At stake is the evaluation of a given model with respect to another one, and the potential correction of bias necessary to merge two databases. We address this from a domain adaptation perspective. We first represent the data using non-linear dimensionality reduction on each database. Then, we formulate the mapping between databases using cases that are shared between the two databases: either as a linear change of basis derived from the learnt eigenvectors, or as a non-linear regression based on the low-dimensional coordinates from each database. We demonstrate these concepts by examining the principal variations in deformation patterns obtained from two cardiac biomechanical models personalized to 20 and 15 real healthy cases, respectively, from which 11 cases were simulated with both models.