An Inverse Eikonal Method for Identifying Ventricular Activation Sequences from Epicardial A Maps
15.07.2019 - 15.07.2019
USI Lugano Campus - Lugano
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An Inverse Eikonal Method for Identifying Ventricular Activation Sequences from Epicardial Activation Maps

Speaker: Thomas Grandits, TU Graz, Austria
Date: Monday, July 15, 2019
Place: USI Lugano Campus, room SI-004 Informatics Building (Via G. Buffi 13)
Time: 14:30- 15:30

In this talk, I will outline the inverse Eikonal problem to infer conduction velocity parameters and initiation timings of earliest activation sites (EAS) in the heart from epicardial arrival time measurements.
The ventricular activation can be represented by an anisotropic Eikonal model solved by the Fast Iterative Method (FIM). Using computed gradients in activation time, a FIM-based minimization scheme is constructed (FIMIN) by solving the inverse Eikonal problem to fit parameters to a target epicardial activation map.
Regularzation methods are first investigated to overcome the severe ill-posedness of the inverse problem in a simplified 2D example. These methods are then employed in an anatomically accurate biventricular model with two realistic activation models of varying complexity -- a simplified trifascicular model (3F) and a topologically realistic model of the His-Purkinje system (HPS). Using epicardial activation maps at full resolution, we first demonstrate that reconstructing the volumetric activation sequence is, in principle, feasible under the assumption of known location of EAS. Robustness of the method under closer to real world conditions was evaluated by reducing the spatial resolution of the observed epicardial activation map and adding noise.
Our results suggest that the FIMIN algorithm is able to robustly recover the full 3D activation sequence using epicardial activation maps at a spatial resolution achievable with current mapping systems and in presence of noise. It can therefore be considered a possible future building block for the inverse ECG problem, estimating mentioned parameters from the torso ECG or body potential surface maps.

Thomas Grandits finished his Master's Degree in Computer Science in 2015 with his Thesis "Optimization Algorithms for Satellite Communication Systems". Since then he has been working on optimization topics across multiple domains, before starting his PhD studies in 2017 with the ILearnHeart project, a project dedicated to the personalization of cardiac simulations.

Host: Prof. Rolf Krause




USI Lugano Campus
Via G. Buffi 13

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