Constructing bilayer and volumetric atrial models at scale
Caroline H. Roney, José Alonso Solís-Lemus, Carlos Barrera, Alexander Zolotarev, Onur M. Ülgen, Eric Kerfoot, Laura Bevis, Semhar Biniam Misghina, Caterina Vidal Horrach, Ovais Ahmed Jaffery, Mahmoud Ehnesh, Cristobal Rodero, Dhani Dharmaprani, Gonzalo R. Ríos‐Muñoz, Anand N. Ganesan, Wilson Good, Aurel Neic, Gernot Plank, Luuk H G A Hopman, Marco Götte, Shohreh Honarbakhsh, Sanjiv M. Narayan, Edward J. Vigmond, Steven Niederer
Abstract
To enable large in silico trials and personalized model predictions on clinical timescales, it is imperative that models can be constructed quickly and reproducibly. First, we aimed to overcome the challenges of constructing cardiac models at scale through developing a robust, open-source pipeline for bilayer and volumetric atrial models. Second, we aimed to investigate the effects of fibres, fibrosis and model representation on fibrillatory dynamics. To construct bilayer and volumetric models, we extended our previously developed coordinate system to incorporate transmurality, atrial regions and fibres (rule-based or data driven diffusion tensor magnetic resonance imaging (MRI)). We created a cohort of 1000 biatrial bilayer and volumetric models derived from computed tomography (CT) data, as well as models from MRI, and electroanatomical mapping. Fibrillatory dynamics diverged between bilayer and volumetric simulations across the CT cohort (correlation coefficient for phase singularity maps: left atrial (LA) 0.27 ± 0.19, right atrial (RA) 0.41 ± 0.14). Adding fibrotic remodelling stabilized re-entries and reduced the impact of model type (LA: 0.52 ± 0.20, RA: 0.36 ± 0.18). The choice of fibre field has a small effect on paced activation data (less than 12 ms), but a larger effect on fibrillatory dynamics. Overall, we developed an open-source user-friendly pipeline for generating atrial models from imaging or electroanatomical mapping data enabling in silico clinical trials at scale ( https://github.com/pcmlab/atrialmtk ).