Local Conduction Velocity Estimation During Wavefront Collisions and Reentrant Scenarios

Conduction velocity
$(CV)$
is an important electrophysiological biomarker for identifying slow-conducting cardiac regions related to pro-arrhythmic behavior. Measurement of
$CV$
is challenging during atypical or irregular activation patterns, such as those present during tachycardia and fibrillation because of the complexity of the underlying mechanics. We propose an approach for
$CV$
estimation designed for taking into account reentries and wavefront collisions. The algorithm is based on a set of constraints imposed on the velocity vector fields. The performance of the proposed algorithm was evaluated against the reference inverse spatial gradient algorithm by using an eikonal reentrant computational model over a left atria anatomy with different mesh resolutions and complexity of the propagation patterns. Our newly proposed
$CV$
mapping is able to identify anchoring zones and the collision of wavefronts without an overestimation of these regions. The performance of our proposed method does improve the reference inverse spatial gradient, with an increased percentage of nodes in which
$CV$
is estimated correctly
$(60.8\pm 7.1\ \%\ vs \ 78.1\pm 10.7\ \%)$
.