A Fast-Marching Approach to Cardiac Electrophysiology Simulation for XMR Interventional Imaging

Abstract

Abstract. Cardiac ablation procedures are becoming more routine to treat arrhythmias. The development of electrophysiological models will allow investigation of treatment strategies. However, current models are computationally expensive and often too complex to be adjusted with current clinical data. In this paper, we have proposed a fast algorithm to solve Eikonal-based models on triangular meshes. These models can be used to extract hidden parameters of the cardiac function from clinical data in a very short time, thus could be used during interventions. We propose a first approach to estimate these parameters, and have tested it on synthetic and real data derived using XMR imaging. We demonstrated a qualitative matching between the estimated parameter and XMR data. This novel approach opens up possibilities to directly integrate modelling in the interventional room.

A predictive method allowing the use of a single ionic model in numerical cardiac electrophysiology

“…The conductivities in the direction n are s i,e (n) = n T σ i,e n and the elliptic operators simplify to ∇ · σ i,e ∇u(x, t) = s i,e ∂ ηη u(x · n, t), where ∂ ηη denotes the second order partial derivative along η = x · n. The results of our asymptotic analysis can thus be applied. For curved wavefronts or conductivity tensors with substantial spatial inhomogeneities, extension of our work is required for instance borrowing ideas from eikonal-curvature or eikonal-diffusion equations [8,18,31].…”

Model-Based Imaging of Cardiac Apparent Conductivity and Local Conduction Velocity for Diagnosis and Planning of Therapy

“…The fast marching methodology applied to solve this nonlinear equation has considerable advantage in terms of obtaining the electrical wave pattern or the isochrones in order of seconds of computational time and hence can be potentially feasible to apply such a model in the clinical setting. A limitation of this model is that the solution methodology is only of first-order accuracy [14]. However, it can be seen that the ¾ node meshes that we use in this study are able to obtain results with sufficient accuracy for the application that we consider (simulate LBBB pathology).…”

“…The nonlinear Equation (1) is solved using a fixed point iterative method combined with a very fast eikonal solver based on a modified anisotropic FMM [14], [15] As the method is based on fast marching which is an Ç´AE ÐÓ ´AEµµ algorithm, where AE denotes the number of points in the mesh, the electrical propagation is solved at a much faster rate as compared to the bi-domain or monodomain equation based models. For example, the solution of a ¼¼¼ node mesh can be achieved in the order of a few seconds [16], and hence the method is suitable for faster computations required in real-time interventional cases.…”

Regional Conduction Velocity Calculation based on Local Activation Times: A Simulation Study on Clinical Geometries

“…In this work the regional CV has been calculated for simulated LAT on clinical geometries. The LATs were simulated using the fast marching (FaMa) [5,6] method and the CV is calculated using the triangulation method [7]. The CV magnitude and directions were visualized on the atrium giving a good understanding of the atrial substrate and the propagation pattern of the depolarization wavefront.…”