Patient-specific generation of the Purkinje network driven by clinical measurements of a normal propagation
| Autor: | Christian Vergara, Simone Palamara, Alfio Quarteroni, Cesarino Pangrazzi, Massimiliano Maines, Fabio Nobile, Domenico Catanzariti, Maurizio Centonze, Giuseppe Vergara, Elena Faggiano |
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| Rok vydání: | 2014 |
| Předmět: |
EnSite NavX system
Time Factors Purkinje fibers Neuromuscular Junction Biomedical Engineering Ventricular endocardium Action Potentials Purkinje Fibers Settore MAT/08 - Analisi Numerica Computational methods medicine Humans Computer Simulation Endocardium Mathematics Computational model Eikonal equation Models Cardiovascular Patient specific Computer Science Applications medicine.anatomical_structure Activation times Ventricle A priori and a posteriori Biomedical engineering |
| Zdroj: | Medical & Biological Engineering & Computing. 52:813-826 |
| ISSN: | 1741-0444 0140-0118 |
| DOI: | 10.1007/s11517-014-1183-5 |
| Popis: | The propagation of the electrical signal in the Purkinje network is the starting point for the activation of the ventricular muscular cells leading to the contraction of the ventricle. In the computational models, describing the electrical activity of the ventricle is therefore important to account for the Purkinje fibers. Until now, the inclusion of such fibers has been obtained either by using surrogates such as space-dependent conduction properties or by generating a network based on an a priori anatomical knowledge. The aim of this work was to propose a new method for the generation of the Purkinje network using clinical measures of the activation times on the endocardium related to a normal electrical propagation, allowing to generate a patient-specific network. The measures were acquired by means of the EnSite NavX system. This system allows to measure for each point of the ventricular endocardium the time at which the activation front, that spreads through the ventricle, has reached the subjacent muscle. We compared the accuracy of the proposed method with the one of other strategies proposed so far in the literature for three subjects with a normal electrical propagation. The results showed that with our method we were able to reduce the absolute errors, intended as the difference between the measured and the computed data, by a factor in the range 9-25 %, with respect to the best of the other strategies. This highlighted the reliability of the proposed method and the importance of including a patient-specific Purkinje network in computational models. |
| Databáze: | OpenAIRE |
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