An effective algorithm for the generation of patient-specific Purkinje networks in computational electrocardiology

Abstract

The Purkinje network is responsible for the fast and coordinated distribution of the electrical impulse in the ventricle that triggers its contraction. Therefore, it is necessary to model its presence to obtain an accurate patient-specific model of the ventricular electrical activation. In this paper, we present an efficient algorithm for the generation of a patientspecific Purkinje network, driven by measures of the electrical activation acquired on the endocardium. The proposed method provides a correction of an initial network, generated by means of a fractal law, and it is based on the solution of Eikonal problems both in the muscle and in the Purkinje network. We present several numerical results both in an ideal geometry with synthetic data and in a real geometry with patient-specific clinical measures. These results highlight an improvement of the accuracy provided by the patientspecific Purkinje network with respect to the initial one. In particular, a cross-validation test shows an accuracy increase of 19% when only the 3% of the total points are used to generate the network, whereas an increment of 44% is observed when a random noise equal to 20% of the maximum value of the clinical data is added to the measures.

A coupled 3D–1D numerical monodomain solver for cardiac electrical activation in the myocardium with detailed Purkinje network

“…Then, in our case we had in general two fronts, one coming from the AV node and another one generated in the myocardium due to pathological conditions (such as the WPW syndrome or the left bundle branch block). We refer the reader to [6,8] for further details.…”

“…For the numerical solution of the EE coupled problem we adopt here the same strategy proposed in [8] for a normal propagation, then extended to treat also pathological conditions in [6]. We refer the reader to these works for further details.…”

“…We cite for example the eikonal model [4,5,6,8], the monodomain model [9,10], and the bidomain model [11,12]. In normal electrical propagation, the electrical signal, originating from the atrioventricular (AV) node, travels along this network and enters the ventricular muscle through the Purkinje-muscle junctions (PMJ).…”

“…In this regard, different coupled models have been considered in the literature. We cite the coupled eikonal/eikonal model [13,5,6,8] (the first model refers to the one used for the network, whereas the second to the one used for the myocardium), the eikonal/monodomain model [4], the monodomain/bidomain model [9], the monodomain/monodomain model [10], and the bidomain/bidomain model [11,12]. When monodomain or bidomain models are considered, the issue of the coupling between cardiac muscle and Purkinje network should be properly addressed.…”

Electrophysiology Model for a Human Heart with Ischemic Scar and Realistic Purkinje Network

“…The resulting PN is fitted to the endocardium, but is not modified according to the LAT observed in the EAM. While methods exist [10,11,15] to fit the PMJ distribution to patient-specific observations of endocardial LAT, the danger of over-fitting the model to available data exists. Increasing the number of PMJs allows fitting of the LAT with arbitrary accuracy at least in the regions where the PN is present, but without necessarily providing meaningful information about the actual morphology of the PN.…”