Roney Caroline H, Solis Lemus Jose Alonso, Lopez Barrera Carlos, Zolotarev Alexander, Ulgen Onur, Kerfoot Eric, Bevis Laura, Misghina Semhar, Vidal Horrach Caterina, Jaffery Ovais A, Ehnesh Mahmoud, Rodero Cristobal, Dharmaprani Dhani, Ríos-Muñoz Gonzalo R, Ganesan Anand, Good Wilson W, Neic Aurel, Plank Gernot, Hopman Luuk H G A, Götte Marco J W, Honarbakhsh Shohreh, Narayan Sanjiv M, Vigmond Edward, Niederer Steven
School of Engineering and Materials Science, Queen Mary University of London, London, UK.
School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Interface Focus. 2023 Dec 15;13(6):20230038. doi: 10.1098/rsfs.2023.0038. eCollection 2023 Dec 6.
To enable large 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 clinical trials at scale (https://github.com/pcmlab/atrialmtk).
为了在临床时间尺度上进行大型试验和个性化模型预测,模型必须能够快速且可重复地构建。首先,我们旨在通过开发一个用于双层和容积性心房模型的强大开源管道,克服大规模构建心脏模型的挑战。其次,我们旨在研究纤维、纤维化和模型表示对颤动动力学的影响。为了构建双层和容积性模型,我们扩展了我们之前开发的坐标系,以纳入透壁性、心房区域和纤维(基于规则或数据驱动的扩散张量磁共振成像(MRI))。我们创建了一组1000个从计算机断层扫描(CT)数据衍生的双心房双层和容积性模型,以及来自MRI和电解剖标测的模型。在CT队列中,双层和容积性模拟之间的颤动动力学存在差异(相位奇点图的相关系数:左心房(LA)0.27±0.19,右心房(RA)0.41±0.14)。添加纤维化重塑可稳定折返并减少模型类型的影响(LA:0.52±0.20,RA:0.36±0.18)。纤维场的选择对起搏激活数据的影响较小(小于12毫秒),但对颤动动力学的影响较大。总体而言,我们开发了一个开源的用户友好管道,用于从成像或电解剖标测数据生成心房模型,从而能够进行大规模的临床试验(https://github.com/pcmlab/atrialmtk)。