Meshless Electrophysiological Modeling of Cardiac Resynchronization Therapy—Benchmark Analysis with Finite-Element Methods in Experimental Data

• Published in: Applied sciences 2022-07, Vol.12 (13), p.6438
• Main Authors: Albors, Carlos, Lluch, Èric, Gomez, Juan Francisco, Cedilnik, Nicolas, Mountris, Konstantinos A., Mansi, Tommaso, Khamzin, Svyatoslav, Dokuchaev, Arsenii, Solovyova, Olga, Pueyo, Esther, Sermesant, Maxime, Sebastian, Rafael, Morales, Hernán G., Camara, Oscar
• Format: Article
• Language: English
• Subjects: Benchmarks; cardiac resynchronization therapy; Cardiology and cardiovascular system; Computer applications; Computer Science; Connectivity; CRT-EPiggy19 challenge; Data collection; Datasets; Electrophysiology; Experimental data; Finite element method; Genetic algorithms; Heart; Heart attacks; Heart failure; Human health and pathology; Life Sciences; Magnetic resonance imaging; Mathematical models; Medical Imaging; Meshless methods; meshless model; Methods; Modeling and Simulation; Optimization; parameter optimisation; Patients; smoothed particle hydrodynamics; Velocity distribution
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12136438

Computational models of cardiac electrophysiology are promising tools for reducing the rates of non-response patients suitable for cardiac resynchronization therapy (CRT) by optimizing electrode placement. The majority of computational models in the literature are mesh-based, primarily using the finite element method (FEM). The generation of patient-specific cardiac meshes has traditionally been a tedious task requiring manual intervention and hindering the modeling of a large number of cases. Meshless models can be a valid alternative due to their mesh quality independence. The organization of challenges such as the CRT-EPiggy19, providing unique experimental data as open access, enables benchmarking analysis of different cardiac computational modeling solutions with quantitative metrics. We present a benchmark analysis of a meshless-based method with finite-element methods for the prediction of cardiac electrical patterns in CRT, based on a subset of the CRT-EPiggy19 dataset. A data assimilation strategy was designed to personalize the most relevant parameters of the electrophysiological simulations and identify the optimal CRT lead configuration. The simulation results obtained with the meshless model were equivalent to FEM, with the most relevant aspect for accurate CRT predictions being the parameter personalization strategy (e.g., regional conduction velocity distribution, including the Purkinje system and CRT lead distribution).