Generating Purkinje networks in the human heart

Abstract

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.

Human ventricular activation sequence and the simulation of the electrocardiographic QRS complex and its variability in healthy and intraventricular block conditions

“…Previous computational studies have also shown agreement in diseased QRS biomarkers associated to left bundle branch block, using either a similar modelling approach to ours for the human activation sequence, 18 or more detailed anatomical representations of the human Purkinje network 16 . Sahli et al 17 also addressed the modelling of right bundle branch block using detailed Purkinje trees, however, without probing its impact on the precordial leads, which are the derivations used in its clinical diagnosis. To the best of our knowledge, modelling of fascicular hemiblocks on the human QRS complex has not been previously addressed in the literature.…”

“…These vary in generality, from specifying activation times analytically by means of a parameterized sequence, 15 to the creation of idealized Purkinje networks including explicit Purkinje-muscle junctions 16 , 17 . They also differ in computational complexity, from the semi-automatic generation of activation profiles, 10 , 18 to more iterative and interactive parametrization processes to converge on a personalized activation sequence 16 .…”

The importance of mechano-electrical feedback and inertia in cardiac electromechanics

“…For the purely electrochemical component, we adopt a modified version of the Aliev-Panfilov model for ionic current [2, 27, 49], $F_{\mathrm{e}}^{\Phi }=c_1\Phi \left[\Phi -\alpha \right]\left[\Phi -1\right]-c_{2}r\Phi ,$where the cubic polynomial term controls the fast upstroke of the action potential through the parameters c 2 and α [15, 40], and the coupling term controls the slow repolarization through the recovery variable r [2]. We treat the recovery variable as an internal variable, which evolves according to the kinetic equation, $$\dot{r}=\left[\gamma +r{\mu }_1/\left[{\mu }_2+\Phi \right]\right]\left[-r-c_2\Phi \left[\Phi -b-1\right]\right],$$where the recovery parameters γ , μ 1 , μ 2 and b control the restitution behavior [2].…”

“…Figure 4, left, illustrates the 20 mm ×20 mm × 10 mm block to represent a region of the cardiac wall, which we excite in three arbitrary elements on the endocardial surface with a body flux of 300 mV/(ms · mm 3 ). This boundary condition mimics the activation by the Purkinje network, which is only connected at discrete points to the myocardial tissue [49]. We fix all faces of the block in their normal direction except the epicardial surface and we set Δ x = 0.5 mm and Δ t = 0.05 ms. As an indicator of conduction velocity in this irregular activation pattern, we use the time to completely activate the block.…”

Integrated Heart—Coupling multiscale and multiphysics models for the simulation of the cardiac function

“…While on idealized representation of the LV an analytical description of the fiber field can be given [109,204], on biventricular patientspecific domains one needs to employ a reconstruction algorithm. To this aim, several algorithms have been proposed [21,277,178,223], and the one we employ herein, originally proposed in [215], is presented below. Myocardial fiber orientations can in principle be recovered in vivo from diffusion magnetic resonance imaging by identifying the dominant direction of diffusion as that of the mean fiber direction.…”