Qualitative assessment of myocardial gray zone in LGE-CMRimaging

Our ongoing research is centered on developing patient-customized computational heart models to study arrhythmia mechanisms and risk assessment. The long-term goal is improving patient selection for a cardio-defibrillator implantation. This device is necessary when the normal pattern of electrical propagation is disrupted in a way that causes the heart rhythm to be so disorganized that the pumping function is compromised. This type of arrhythmia is named fibrillation and can only be stopped by an immediate intervention, if not, the brain irrigation is intercepted resulting in sudden cardiac death. Our approach to assessing if an individual is at risk of going through this event is to study the electrical propagation on their own heart using a parallel model. In the present article, we propose a novel method to add a qualitative rating to scarred myocardium. A left ventricle computational model is built, based on an anonymized cardiac magnetic resonance data set with visible scars. The scarred areas are identified and divided into the scar’s core, made of dead cells where the conductivity is null, and the heterogeneous or gray zones where different levels of fibrosis coexist, forming both viable and nonviable paths for the depolarization wave. After this major segmentation, the gray zones are split into subsets and the conductivities are computed according to the intensity of the pixels. The number of subsets can be set between two and five. In this work, we are considering two scenarios: with one gray zone level and two. Resulting in two possible patterns the depolarization signal can obey. Electrophysiological simulations are performed for each scenario using the open-source software CHASTE (Cancer, Heart, and Soft Tissue Environment). The results of this phase are used to analyze how the depolarization patterns behave according to the granularity degree set for the gray zones.