A smoothed boundary bidomain model for cardiac simulations in anatomically detailed geometries

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

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.

[Developments of ex vivo cardiac electrical mapping and intelligent labeling of atrial fibrillation substrates]

Cardiac three-dimensional electrophysiological labeling technology is the prerequisite and foundation of atrial fibrillation (AF) ablation surgery, and invasive labeling is the current clinical method, but there are many shortcomings such as large trauma, long procedure duration, and low success rate. In recent years, because of its non-invasive and convenient characteristics, ex vivo labeling has become a new direction for the development of electrophysiological labeling technology. With the rapid development of computer hardware and software as well as the accumulation of clinical database, the application of deep learning technology in electrocardiogram (ECG) data is becoming more extensive and has made great progress, which provides new ideas for the research of ex vivo cardiac mapping and intelligent labeling of AF substrates. This paper reviewed the research progress in the fields of ECG forward problem, ECG inverse problem, and the application of deep learning in AF labeling, discussed the problems of ex vivo intelligent labeling of AF substrates and the possible approaches to solve them, prospected the challenges and future directions for ex vivo cardiac electrophysiology labeling.