This repository contains the code developed for the study: Digital Twinning of the Human Ventricular Activation Sequence to Clinical 12-Lead ECGs and Magnetic Resonance Imaging Using Realistic Purkinje Networks for in Silico Clinical Trials
Accepted in Medical Image Analysis: https://doi.org/10.48550/arXiv.2306.13740
To numerically solve the monodomain equation we use an open-source high-performance GPU solver called MonoAlg3D_C, which is publicly available at the following repository:
Oliveira RS, Rocha BM, Burgarelli D, Meira Jr W, Constantinides C, dos Santos RW. Performance evaluation of GPU parallelization, space‐time adaptive algorithms, and their combination for simulating cardiac electrophysiology. Int J Numer Meth Biomed Engng. 2018;34:e2913. https://doi.org/10.1002/cnm.2913
To generate the extra branches for the biophysically-detailed Purkinje networks we use the same Purkinje generation method given in Berg et al. (2023), which is also publicly available in an open-source repository:
Berg, L. A, Rocha, B. M., Oliveira, R. S., Sebastian, R., Rodriguez, B., de Queiroz, R. A. B., Cherry, E. M., dos Santos, R. W. Enhanced optimization-based method for the generation of patient-specific models of Purkinje networks. Nature Scientific Reports. https://www.doi.org/10.1038/s41598-023-38653-1