A Matlab Toolbox for cardiac electrophysiology simulations on patient-specific geometries.

Abstract

In this paper, we present CardioMat, a Matlab toolbox for cardiac electrophysiology simulation based on patient-specific anatomies. The strength of CardioMat is the easy and fast construction of electrophysiology cardiac digital twins from segmented anatomical images in a general-purpose software such as Matlab. CardioMat implements a quasi-automatic pipeline that guides the user toward the construction of anatomically detailed cardiac electrophysiology models. Importantly, the CardioMat framework includes the generation of physiologically plausible fiber orientation and Purkinje networks. The main novelty of our framework is its ability to handle voxel-based geometries as produced by segmentation procedures directly, without the need for an unstructured mesh. Indeed, the CardioMat monodomain solver uses a smoothed boundary approach and runs completely on GPU for fast simulations. We employed CardioMat in different application scenarios to show its potentialities and provide preliminary assessment of the feasibility, diagnostic performance, and accuracy of the toolbox. In particular, we showed that CardioMat simulations derived from post-infarction patients hold high sensitivity, specificity, predictive value, and accuracy for localization of deceleration zones in sinus rhythm.