Effect of Segmentation Uncertainty on the ECGI Inverse Problem Solution and Source Localization
Year: 2022
Type: conference-paper
Abstract: Electrocardiographic Imaging (ECGI) is a promising tool to non-invasively map the electrical activity of the heart using body surface potentials (BSPs) and the patient specific anatomical data. One of the first steps of ECGI is the segmentation of the heart and torso geometries. In the clinical practice, the segmentation procedure is not fully-automated yet and is in consequence operator-dependent. We expect that the inter-operator variation in cardiac segmentation would influence the ECGI solution. This effect remains however non quantified. In the present work, we study the effect of segmentation variability on the ECGI estimation of the cardiac activity with 262 shape models generated from fifteen different segmentations. Therefore, we designed two test cases: with and without shape model uncertainty. Moreover, we used four cases for ectopic ventricular excitation and compared the ECGI results in terms of reconstructed activation times and excitation origins. The preliminary results indicate that a small variation of the activation maps can be observed with a model uncertainty but no significant effect on the source localization is observed.
Source: Computing in cardiology
Institutions: Institut national de recherche en sciences et technologies du numérique, Université de Bordeaux, Electrophysiology and Heart Modeling Institute, Centre Inria de l'université de Bordeaux, CARMEN: Modélisation et calculs pour l'électrophysiologie cardiaque, University Medical Center Utrecht, Institute of Measurement Science of the Slovak Academy of Sciences, Northeastern University, University of Utah
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Funders: Agence Nationale de la Recherche, Dalhousie University, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of General Medical Sciences, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institute of Biomedical Imaging and Bioengineering
Awards: Plateforme multi-modale d'exploration en cardiologie, Center for Integrative Biomedical Computing, G657782616, Integration of Uncertainty Quantification with SCIRun Bioelectric Field Simulation Pipeline, Center for Integrative Biomedical Computing Legacy Transition, G7240651115, New 3D and 4D Image-Based Clinical Measures of Atrial Structural and Functional Remodeling in Atrial Fibrillation, Anatomy Directly from Imagery: General-purpose, Scalable, and Open-source Machine Learning Approaches, ShapeWorksStudio: An Integrative, User-Friendly, and Scalable Suite for Shape Representation and Analysis
OpenAlex ID: W4316097156