Redirecting to original paper in 30 seconds...

Click below to go immediately or wait for automatic redirect

arxiv_cv 75% Match Research Paper Researchers in biomedical engineering and medical imaging,Cardiologists,Applied mathematicians,Computational scientists 1 day ago

Finite element-based space-time total variation-type regularization of the inverse problem in electrocardiographic imaging

robotics › navigation
📄 Abstract

Abstract: Reconstructing cardiac electrical activity from body surface electric potential measurements results in the severely ill-posed inverse problem in electrocardiography. Many different regularization approaches have been proposed to improve numerical results and provide unique results. This work presents a novel approach for reconstructing the epicardial potential from body surface potential maps based on a space-time total variation-type regularization using finite elements, where a first-order primal-dual algorithm solves the underlying convex optimization problem. In several numerical experiments, the superior performance of this method and the benefit of space-time regularization for the reconstruction of epicardial potential on two-dimensional torso data and a three-dimensional rabbit heart compared to state-of-the-art methods are demonstrated.
Authors (6)
Manuel Haas
Thomas Grandits
Thomas Pinetz
Thomas Beiert
Simone Pezzuto
Alexander Effland
Submitted
August 21, 2024
arXiv Category
math.NA
arXiv PDF

Key Contributions

Presents a novel finite element-based space-time total variation-type regularization approach for the ill-posed inverse problem in electrocardiographic imaging. This method, solved via a primal-dual algorithm, demonstrates superior performance in reconstructing epicardial potentials compared to state-of-the-art methods.

Business Value

Enables more accurate and reliable non-invasive mapping of cardiac electrical activity, potentially leading to improved diagnosis and treatment of cardiac arrhythmias and other heart conditions. Advances the field of cardiac imaging.

Paper Metadata

Innovation Type

Novel method/algorithm

Deployment Feasibility

Low to Moderate. This is a highly specialized computational method requiring significant expertise and computational resources. Integration into clinical practice would involve complex validation and hardware/software development.

Limitations Addressed

Addresses the severely ill-posed nature of reconstructing cardiac electrical activity from body surface measurements, which often leads to non-unique or numerically unstable results. Solves limitations of existing regularization approaches.

Technical Tags

inverse problemelectrocardiographyfinite elementsspace-time regularizationtotal variationepicardial potentialbody surface potentialsconvex optimizationprimal-dual algorithmcardiac imaging

Research Topics

Inverse ProblemsMedical ImagingElectrophysiologyNumerical MethodsSignal Processing

Methods & Architectures

Finite element-based space-time total variation regularizationFirst-order primal-dual algorithmConvex optimization

Applications & Tasks

Medical Diagnostics Cardiology Biomedical Engineering Medical Imaging Reconstructing cardiac electrical activitySolving ill-posed inverse problemsImproving accuracy of epicardial potential estimationEnhancing uniqueness of solutions Inverse problem reconstructionEpicardial potential mappingCardiac electrical activity imaging

Related Fields

Biomedical EngineeringApplied MathematicsMedical ImagingCardiologyNumerical Analysis

Keywords

inverse problemelectrocardiographyfinite elementsspace-time regularizationtotal variationepicardial potentialcardiac imagingnumerical methodsconvex optimizationbiomedical engineering

Academic Context

#Inverse Problems#Medical Imaging#Electrophysiology#Numerical Methods#Signal Processing

Commercial Potential

Potential Products
Advanced cardiac imaging softwareDiagnostic tools for arrhythmia analysisSimulation software for cardiac electrophysiology research
Target Industries
HealthcareMedical DevicesBiotechnologyResearch Institutions
Use Case Examples
Non-invasive mapping of electrical activity on the heart surfaceImproving the accuracy of ECG-based diagnosis of heart conditionsSimulating cardiac electrical propagation for research purposes
Competitive Edge
Offers a novel regularization technique (space-time total variation) within a finite element framework, providing superior reconstruction accuracy for the challenging inverse problem in ECG imaging compared to existing methods.
Market Opportunity
Significant market for advanced cardiac diagnostic and imaging technologies.
Revenue Models
Licensing of the reconstruction algorithmdevelopment of specialized medical devices.

Resource Requirements

Compute Needs
High (for solving complex optimization problems)
Data Requirements
Body surface potential measurements and corresponding anatomical models.
Deployment Constraints
Requires specialized hardware and software for data acquisition and processing. Clinical validation is extensive.
Scalability
Scalability depends on the computational efficiency of the finite element method and the optimization solver.
Regulatory Considerations
FDA approval (for diagnostic devices)

Production Readiness

Maturity Level

Research

Time to Market

3-5 years

Patent Potential

Moderate (for novel regularization method)

View Full Paper Back to Papers