Patient-specific computational generation of the Purkinje network driven by clinical measuraments

09/2013

Patient-specific computational generation of the Purkinje network driven by clinical measuraments

Saturday 23rd February 2013

Vergara, C.; Palamara, S.; Catanzariti, D.; Pangrazzi, C.; Nobile, F.; Centonze, M.; Faggiano, E.; Maines, M.; Quarteroni, A.; Vergara, G.

Rationale: The propagation of the electrical signal in the Purkinje network is the starting point of the activation of the muscular cells in the ventricle and of the contraction of the heart. Anomalous propagation in such a network can cause disorders such as ventricular fibrillation. In the computational models describing the electrical activity of the ventricle is therefore important to account for the Purkinje fibers. Objective: Aim of this work is to apply to real cases a method for the generation of the Purkinje network driven by patient-specific measures of the activation on the endocardium, to compute the activation maps in the ventricle and to compare the accuracy with that of other strategies proposed so far in the literature. Methods and Results: We consider MRI images of two patients and data of the activation times on the endocardium acquired by means of the EnSite NavX system. To generate the Purkinje network we use a fractal law driven by the measures. Our results show that for a normal activation our algorithm is able to reduce considerably the errors (19.9±5.3% with our algorithm vs 33.9±6.8% with the best of the other strategies for patient 1, and 28.6±6.0% vs 63.9±8.6% for patient 2). Conclusions: In this work we showed the reliability of the proposed method to generate a patient-specific Purkinje network. This allowed to improve the accuracy of computational models for the description of the electrical activation in the ventricle.