Generating Purkinje networks in the human heart

By F. Sahli Costabal, Daniel E. Hurtado, Ellen Kuhl. 2015

118 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Paris Perdikaris
2. F. Sahli Costabal
3. Ellen Kuhl
4. Daniel E. Hurtado
5. Yibo Yang

In Cardiology and Cardiovascular Medicine

1. Paris Perdikaris
2. F. Sahli Costabal
3. Daniel E. Hurtado
4. Ellen Kuhl
5. Yibo Yang

Citations in the corpus

• Physics-Informed Neural Networks for Cardiac Activation Mapping (2020, background): “First, we have ignored the anisotropy in conduction of cardiac tissue [19, 37].”
• Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models (2019, background): “We choose this particular parameter because we know that, besides controlling the action potential duration [34, 13], it controls how much the spiral wave meanders in the domain.”
• Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification (2019, methodology): “To model the Purkinje fiber network, we generate a fractal tree that grows on the endocardial surface of the heart [63].”
• The importance of mechano-electrical feedback and inertia in cardiac electromechanics (2017, unknown): “Nonetheless, we plan to incorporate our findings into a four-chamber, high resolution heart simulator [12] and explore the influence of inertia and mechano-electrical currents on clinically relevant variables such as electrocardiograms [28,37,43,44] and pressure–volume loops [45].”