Comparison of LGE MRI Scar Identification Methods for Atrial Computational Modeling.

Abstract

Identification of patient-specific scar and fibrosis is a critical step in the personalization of cardiac computational models. Late gadolinium enhanced cardiac magnetic resonance imaging (LGE-cMRI) is often used to identify patient anatomy, as well as tissue fibrosis and scar. Automated methods to identify scar from LGE-cMRI exist. Still, there is no clear consensus as to which is best in the context of patient-specific computational modeling of atrial fibrillation. There has been no substantial investigation into the effects that variability in scar may have on downstream patient-specific simulations. This study compares the distribution of scar patterns generated via automated LGE-cMRI analysis alongside human-guided scar identification. We assess the effects each identified scar pattern has on downstream computational modeling outputs by comparing the number of stable re-entrant arrhythmias induced In Silico in atrial fibrillation. We find both substantial disagreement between scar patterns identified via automated and human-guided methods, as well as sensitivity in the arrhythmia simulation outcomes across scar patterns. These results highlight the sensitivity of such computational models to these input parameters and enforce the need for robust personalization tools in the cardiac modeling field.