DefibViz: A Visualization Tool for the Assessment of Electrode Parameters on Transthoracic Defibrillation Thresholds

Abstract

DefibViz is a software application developed for defibrillation simulation and visualization. It exploits both surface techniques and methods for the interactive exploration of volumetric datasets for the analysis of transthoracic defibrillation simulation results. DefibViz\ has a graphical user interface for the specification of the shape, size, position, and applied voltage of a defibrillator's electrodes. An option is provided for using 3-D slice plane widgets, which operate on the volumetric datasets, such that the distribution of the voltage gradient induced by an electric shock can be visually inspected in various tissues throughout the myocardium and torso. One goal of DefibViz is to enhance understanding of how electrode parameters relate to the change of the voltage gradient distribution throughout the heart, which may help lead to optimal defibrillator design. DefibViz; is significant, in that, it is built by using an open-source graphics and visualization framework providing a platform for subsequent modifiability and extensibility. Moreover, it integrates simulation and visualization techniques, which previously required the running of several independent software executables, into an enhanced, seamless, and comprehensive software application.

Placement of implantable cardioverter‐defibrillators in paediatric and congenital heart defect patients: a pipeline for model generation and simulation prediction of optimal configurations

“…While the model presented here is, to the best of our knowledge, the first MRI‐derived heart–torso model of a CHD patient reconstructed from MRI scans, attempts to construct heart–torso models from MRI datasets have been made in the past, but only for patients with structurally normal hearts. Such models have been employed in the study of defibrillation (Eason et al 1998; de Jongh et al 1999; Russomanno et al 2008) as well as for other uses (Tilg et al 2002; Berger et al 2006; Vanheusden et al 2012). None of these models, however, included an active ventricular model, and they were of much coarser resolution than our model – especially in the ventricular.…”

“…This criterion is based on the critical mass hypothesis, which postulates that a defibrillation shock is successful if it produces a strong Φ e gradient over a large amount of ventricular tissue mass (Zipes et al 1975; Zhou et al 1993). This surrogate DFT criterion has been used in a number of computational defibrillation studies (Eason et al 1998; de Jongh et al 1999; Hunt & de Jongh Curry, 2004, 2006; Russomanno et al 2008; Jolley et al 2010). While Φ e gradients are a determinant of post‐shock activity in the heart, other mechanisms are at play as well (Knisley et al 1999; Trayanova, 2001): not only Φ e gradients but also cardiac tissue structure is responsible for virtual electrode polarizations (VEPs; depolarizing and hyperpolarizing changes in membrane potential in response to an electric field) that can generate or abolish wavefronts (Sobie et al 1997; Efimov et al 1998; Trayanova et al 1998; Efimov & Ripplinger, 2006).…”

How computer simulations of the human heart can improve anti‐arrhythmia therapy

“…4A-C; it was used for the modelling of subcutaneous ICD electrodes (Jolley et al 2010). While such model studies (Eason et al 1998;de Jongh et al 1999;Hunt & de Jongh Curry, 2004, 2006Jolley et al 2008Jolley et al , 2010Russomanno et al 2008) have provided an understanding of the current flow in the human resulting from the various placements of the defibrillation leads, they did not simulate the process of defibrillation, where the cell membrane responses to electric shocks have to be incorporated, but rather used the criterion of static extracellular potential gradient values above 5 V cm −1 in more than 95% of the volume of the passive ventricles during the shock as a surrogate for the DFT (Fig. 4D).…”

“…In addition to not incorporating the processes taking place during activation and repolarization of the heart, heart-torso defibrillation models historically involved other limitations resulting from the lack of appropriate imaging data. These included the use of a canine heart model within the human torso (Eason et al 1998), not accounting for fibre architecture and tissue anisotropy (de Jongh et al 1999;Tilg et al 2002;Berger et al 2006;Russomanno et al 2008;Vanheusden et al 2012), or only estimating the myocardial surfaces based on a certain distance from ventricular blood masses, but not detecting and modelling the myocardial volume itself (Tilg et al 2002;Berger et al 2006). Limitations associated with the lack of appropriate imaging data have been recently overcome, and human heart-torso models aimed at determining the DFTs associated with different ICD configurations (both transvenous and extracardiac) in a variety of patient groups, including paediatric and congenital heart disease (CHD) patients (Jolley et al 2008(Jolley et al , 2010 have been developed from torso imaging data.…”

“…; Russomanno et al . ; Vanheusden et al . ), or only estimating the myocardial surfaces based on a certain distance from ventricular blood masses, but not detecting and modelling the myocardial volume itself (Tilg et al .…”