Generating Purkinje networks in the human heart

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• 作者：Francisco Sahli Costabal^a ; Daniel E. Hurtado^b ; Ellen Kuhl^c ; ^{ekuhl@stanford.edu" class="auth_mail" title="E-mail the corresponding author} ;
• 关键词：Cardiac modeling ; Finite element method ; Excitation ; Electrophysiology ; Purkinje network ; Bundle branch block
• 刊名：Journal of Biomechanics
• 出版年：2016
• 出版时间：16 August 2016
• 年：2016
• 卷：49
• 期：12
• 页码：2455-2465
• 全文大小：5764 K

文摘

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±3 mm, the surface density of our new Purkije network is twice as dense as the conventional network with 22±7 mm. 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.