Sahli Costabal Francisco, Hurtado Daniel E, Kuhl Ellen
Department of Mechanical Engineering, Stanford University, Stanford, CA, USA.
Department of Structural and Geotechnical Engineering and Institute of Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
J Biomech. 2016 Aug 16;49(12):2455-65. doi: 10.1016/j.jbiomech.2015.12.025. Epub 2015 Dec 22.
The Purkinje network is an integral part of the excitation system in the human heart. Yet, to date, there is no in vivo imaging technique to accurately reconstruct its geometry and structure. Computational modeling of the Purkinje network is increasingly recognized as an alternative strategy to visualize, simulate, and understand the role of the Purkinje system. However, most computational models either have to be generated manually, or fail to smoothly cover the irregular surfaces inside the left and right ventricles. Here we present a new algorithm to reliably create robust Purkinje networks within the human heart. We made the source code of this algorithm freely available online. Using Monte Carlo simulations, we demonstrate that the fractal tree algorithm with our new projection method generates denser and more compact Purkinje networks than previous approaches on irregular surfaces. Under similar conditions, our algorithm generates a network with 1219±61 branches, three times more than a conventional algorithm with 419±107 branches. With a coverage of 11±3mm, the surface density of our new Purkije network is twice as dense as the conventional network with 22±7mm. To demonstrate the importance of a dense Purkinje network in cardiac electrophysiology, we simulated three cases of excitation: with our new Purkinje network, with left-sided Purkinje network, and without Purkinje network. Simulations with our new Purkinje network predicted more realistic activation sequences and activation times than simulations without. Six-lead electrocardiograms of the three case studies agreed with the clinical electrocardiograms under physiological conditions, under pathological conditions of right bundle branch block, and under pathological conditions of trifascicular block. Taken together, our results underpin the importance of the Purkinje network in realistic human heart simulations. Human heart modeling has the potential to support the design of personalized strategies for single- or bi-ventricular pacing, radiofrequency ablation, and cardiac defibrillation with the common goal to restore a normal heart rhythm.
浦肯野网络是人体心脏兴奋系统的一个组成部分。然而,迄今为止,尚无体内成像技术能够准确重建其几何形状和结构。浦肯野网络的计算建模日益被视为一种可视化、模拟和理解浦肯野系统作用的替代策略。然而,大多数计算模型要么必须手动生成,要么无法平滑覆盖左心室和右心室内的不规则表面。在此,我们提出一种新算法,能够在人体心脏内可靠地创建健壮的浦肯野网络。我们将该算法的源代码免费发布在网上。通过蒙特卡洛模拟,我们证明,采用我们新的投影方法的分形树算法,在不规则表面上生成的浦肯野网络比以前的方法更密集、更紧凑。在相似条件下,我们的算法生成的网络有1219±61个分支,是传统算法(419±107个分支)的三倍。我们新的浦肯野网络的表面密度为11±3毫米,是传统网络(22±7毫米)的两倍。为证明密集的浦肯野网络在心脏电生理学中的重要性,我们模拟了三种兴奋情况:使用我们新的浦肯野网络、使用左侧浦肯野网络以及不使用浦肯野网络。使用我们新的浦肯野网络进行的模拟预测的激活序列和激活时间比不使用该网络的模拟更符合实际情况。三个案例研究的六导联心电图在生理条件下、右束支传导阻滞的病理条件下以及三分支传导阻滞的病理条件下均与临床心电图一致。综上所述,我们的结果证实了浦肯野网络在真实人体心脏模拟中的重要性。人体心脏建模有潜力支持单心室或双心室起搏、射频消融和心脏除颤等个性化策略的设计,共同目标是恢复正常心律。