Palamara Simone, Vergara Christian, Catanzariti Domenico, Faggiano Elena, Pangrazzi Cesarino, Centonze Maurizio, Nobile Fabio, Maines Massimiliano, Quarteroni Alfio
Modellistica e Calcolo Scientifico (MOX), Dipartimento di Matematica, Politecnico di Milano, Milan, Italy.
Int J Numer Method Biomed Eng. 2014 Dec;30(12):1558-77. doi: 10.1002/cnm.2689. Epub 2014 Oct 22.
To properly describe the electrical activity of the left ventricle, it is necessary to model the Purkinje fibers, responsible for the fast and coordinate ventricular activation, and their interaction with the muscular propagation. The aim of this work is to propose a methodology for the generation of a patient-specific Purkinje network driven by clinical measurements of the activation times related to pathological propagations. In this case, one needs to consider a strongly coupled problem between the network and the muscle, where the feedback from the latter to the former cannot be neglected as in a normal propagation. We apply the proposed strategy to data acquired on three subjects, one of them suffering from muscular conduction problems owing to a scar and the other two with a muscular pre-excitation syndrome (Wolff-Parkinson-White). To assess the accuracy of the proposed method, we compare the results obtained by using the patient-specific Purkinje network generated by our strategy with the ones obtained by using a non-patient-specific network. The results show that the mean absolute errors in the activation time is reduced for all the cases, highlighting the importance of including a patient-specific Purkinje network in computational models.
为了恰当地描述左心室的电活动，有必要对负责快速且协调的心室激活的浦肯野纤维及其与肌肉传播的相互作用进行建模。这项工作的目的是提出一种方法，用于生成由与病理传播相关的激活时间的临床测量驱动的患者特异性浦肯野网络。在这种情况下，需要考虑网络与肌肉之间的强耦合问题，其中与正常传播不同，后者向前者的反馈不能被忽略。我们将所提出的策略应用于在三名受试者身上获取的数据，其中一名受试者由于瘢痕而存在肌肉传导问题，另外两名患有肌肉预激综合征（沃尔夫 - 帕金森 - 怀特综合征）。为了评估所提出方法的准确性，我们将使用我们的策略生成的患者特异性浦肯野网络所获得的结果与使用非患者特异性网络所获得的结果进行比较。结果表明，所有病例中激活时间的平均绝对误差都有所降低，突出了在计算模型中纳入患者特异性浦肯野网络的重要性。
