Estimation and Validation of Cardiac Conduction Velocity and Wavefront Reconstruction Using Epicardial and Volumetric Data

Good, Wilson W.; Gillette, Karli; Zenger, Brian; Bergquist, Jake A; Rupp, Lindsay C; Tate, Jess; Anderson, Devan; Gsell, Matthias A. F.; MacLeod, Robert S.

doi:10.1109/tbme.2021.3069792

Cited by 15 publications

(13 citation statements)

References 21 publications

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“…In our opinion, there is a clear need for benchmark data for evaluating algorithms for calculating conduction velocity. Reproducible simulated data where the ‘ground truth’ LAT (and therefore CV) is known everywhere would probably be the easiest solution, especially as computational electrophysiology simulations are extremely well developed [ 113 , 114 ]. Open-source code for methods is important [ 23 ] but is only part of the solution, as its existence can imply that the burden of quantitative comparison (against an ever increasing cannon of previous methods) should be placed entirely on researchers proposing new methods, rather than being shared amongst the research community more broadly.…”

Section: Discussionmentioning

confidence: 99%

Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate

Coveney

Cantwell

Roney

2022

Med Biol Eng Comput

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Characterizing patient-specific atrial conduction properties is important for understanding arrhythmia drivers, for predicting potential arrhythmia pathways, and for personalising treatment approaches. One metric that characterizes the health of the myocardial substrate is atrial conduction velocity, which describes the speed and direction of propagation of the electrical wavefront through the myocardium. Atrial conduction velocity mapping algorithms are under continuous development in research laboratories and in industry. In this review article, we give a broad overview of different categories of currently published methods for calculating CV, and give insight into their different advantages and disadvantages overall. We classify techniques into local, global, and inverse methods, and discuss these techniques with respect to their faithfulness to the biophysics, incorporation of uncertainty quantification, and their ability to take account of the atrial manifold. Graphical abstract

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Section: Discussionmentioning

confidence: 99%

Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate

Coveney

Cantwell

Roney

2022

Med Biol Eng Comput

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“…A first class of methods estimates the local CV θ(x) at some given location x on the basis of the temporal differences in activation between x and its neighbors [17,[28][29][30][31]. These methods are easy to implement and very fast to execute.…”

Section: Data-driven Methodsmentioning

confidence: 99%

Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps

Herrera,

Grandits,

Plank

et al. 2022

Preprint

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We propose FiberNet, a method to estimate in-vivo the cardiac fiber architecture of the human atria from multiple catheter recordings of the electrical activation. Cardiac fibers play a central role in the electromechanical function of the heart, yet they are difficult to determine in-vivo, and hence rarely truly patient-specific in existing cardiac models. FiberNet learns the fibers arrangement by solving an inverse problem

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“…Diffusion in both myocardium and endocardium were varied, taking as reference the diffusion value in the work from Bueno-Orovio et al [19] for the compact myocardium; while for the endocardium we employed a higher diffusion to account for the Purkinje network and obtain physiological values of both total activation times (TATs) and conduction velocity (CVs). The selected myocardial and endocardial diffusion values were determined in order to obtain human physiological values of QRS interval and conduction velocities as reported [20] and [21] in all geometries. This objective was important to discard confounding aspects that could arise due to diffusion coefficient differences between models.…”

Section: Parameterization Of Diffusionmentioning

confidence: 99%

Ventricular anatomical complexity and gender differences impact predictions from computational models

Gonzalez-Martin

Sacco

Butakoff

et al. 2022

Preprint

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The aim of this work was to analyze the influence of the gender and anatomical details (trabeculations and false tendons) on the electrophysiology of healthy and infarcted myocardium and their relation to the ventricular tachycardia (VT) inducibility. To this end, four anatomically normal, human, biventricular geometries (two male, two female), with identifiable trabeculations, were obtained from high-resolution, ex-vivo MRI and represented by detailed and smoothed geometrical models (with and without the trabeculations). Additionally one model was augmented by a scar. The electrophysiology finite element model (FEM) simulations were carried out, using O’Hara-Rudy human myocyte model, and the comparison of detailed vs smooth, male vs female, as well as normal vs infarcted anatomy was carried out. Finally, the infarcted case was subjected to standard clinical programmed stimulus S1-S4 protocol to identify geometry- and gender-specific VT inducibility. All female hearts presented action potential prolongation and QRS interval lengthening. Smooth geometries showed different QRS pattern and T wave magnitude in comparison to the corresponding trabeculated geometries. A variety of sustained VTs were obtained in the detailed and smoothed male geometries at different pacing locations, which provide evidence of the geometry-dependent differences regarding the prediction of the locations of reentry channels. In the female phenotype, sustained VTs were induced in both detailed and smooth geometries with RV apex pacing, however no consistent reentry channels were identified. Anatomical and physiological cardiac features play an important role defining risk in cardiac disease. These are often excluded from electrophysiological simulations. The assumption that the cardiac endocardium is smooth may inaccurately predict different reentry channel locations in tachycardia inducibility studies.

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Estimation and Validation of Cardiac Conduction Velocity and Wavefront Reconstruction Using Epicardial and Volumetric Data

Cited by 15 publications

References 21 publications

Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate

Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate

Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps

Ventricular anatomical complexity and gender differences impact predictions from computational models

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