Volumetric Non-Invasive Cardiac Mapping for Accessible Global Arrhythmia Characterization

2025-10-20

AI Summary

Traditional noninvasive electrocardiographic imaging (ECGI) reconstructs only epicardial electrical activity, limiting its ability to localize arrhythmia origins deep within the myocardium. To address this, we propose a volumetric hierarchical electrophysiological imaging framework that, for the first time, solves the 3D inverse source problem directly from body-surface potential maps using Green’s function modeling—without requiring image guidance or prior anatomical knowledge. This enables noninvasive reconstruction of activation sequences throughout the entire myocardial volume. The method significantly improves localization accuracy in anatomically complex regions, such as ventricular walls and infarct zones. Validation on simulated premature ventricular contractions and a public myocardial infarction dataset shows a 59.3% reduction in geodesic distance error compared to conventional surface-based ECGI. Clinical case results demonstrate strong concordance with invasive electrophysiological mapping and postoperative diagnosis, supporting preprocedural planning and personalized therapy optimization.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Learning with Manifolds

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Cardiac arrhythmias are a major cause of morbidity and mortality increasing the risk of stroke, heart failure, and sudden cardiac death. Imageless electrocardiographic imaging (ECGI) provides a non invasive alternative to electrical mapping from body surface potentials, but conventional ECGI is confined to epicardial reconstructions and can miss arrhythmias originating in deeper myocardium. We address this by reconstructing three dimensional cardiac activity with a volumetric formulation that solves an inverse source problem via Green's functions, enabling full volume activation mapping and improved localization in anatomically complex regions. We evaluate the approach on simulated premature ventricular beats and on four challenging patient cases, a right ventricular outflow tract premature ventricular contraction, a left bundle branch block, a ventricular tachycardia, and Wolff Parkinson White, and additionally assess performance on an open source myocardial infarction dataset. Results show that volumetric ECGI recovers 3D activation and sharpens arrhythmia origin localization, achieving a 59.3% reduction in geodesic error between estimated and simulated origins relative to surface only methods; in patient cases, activation patterns align with clinical diagnoses. Overall, imageless volumetric ECGI offers accessible, non invasive 3D activation mapping that overcomes a core limitation of surface restricted techniques and may improve preprocedural planning, ablation target guidance, and selection or optimization of cardiac resynchronization therapy.

Problem

• Reconstructing 3D cardiac activity for full-volume arrhythmia mapping
• Overcoming surface-only limitations to localize deep myocardial arrhythmias
• Improving non-invasive localization accuracy for complex cardiac conditions

Innovation

• Volumetric formulation solves inverse source problem
• Uses Green's functions for 3D cardiac activity reconstruction
• Enables full volume activation mapping for arrhythmias