Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

An iterative algorithm for myocardial activation time imaging.

R Modre1, B Tilg, G Fischer

  • 1Institute of Biomedical Engineering, Technical University Graz, Inffeldgasse 18, 8010, Graz, Austria. modre@ibmt.tu-graz.ac.at

Computer Methods and Programs in Biomedicine
|November 21, 2000
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Biomedical Signal Processing.

Yearbook of medical informatics·2016
Same author

Patient-specific volume conductor modeling for non-invasive imaging of cardiac electrophysiology.

The open medical informatics journal·2009
Same author

A finite element formulation for atrial tissue monolayer.

Methods of information in medicine·2008
Same author

DMSP--Database for Modeling Signaling Pathways. Combining biological and mathematical modeling knowledge for pathways.

Methods of information in medicine·2008
Same author

Detection of phase singularities in triangular meshes.

Methods of information in medicine·2007
Same author

Fibrillatory conduction in branching atrial tissue--Insight from volumetric and monolayer computer models.

Computer methods and programs in biomedicine·2007
Same journal

SynTME: A tumor microenvironment-aware, pharmacology-inspired multi-stage framework for drug synergy prediction.

Computer methods and programs in biomedicine·2026
Same journal

MMFVS-Net: A triple-symmetric cross-attention network for multimodal optical image fusion and high-accuracy virtual staining of breast cancer tissues.

Computer methods and programs in biomedicine·2026
Same journal

A novel Milstein-stochastic epidemiologically-informed neural network for approaching epidemic dynamics: Application to Mpox disease.

Computer methods and programs in biomedicine·2026
Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
See all related articles

Method A, an iterative algorithm for nonlinear inverse problems, effectively imaged myocardial activation times from magnetocardiography data. It demonstrated superior computational performance and simpler regularization parameter selection compared to Tikhonov

Area of Science:

  • Biomedical Engineering
  • Computational Electromagnetics
  • Medical Imaging

Background:

  • The magnetocardiographic (MCG) inverse problem is crucial for non-invasively mapping cardiac electrical activity.
  • Nonlinear ill-posed problems, common in medical imaging, require robust regularization techniques for accurate solutions.
  • Existing methods for myocardial activation mapping face challenges in computational efficiency and parameter selection.

Purpose of the Study:

  • To apply and evaluate an iterative regularization algorithm (Method A) for solving the MCG inverse problem.
  • To compare the performance of Method A against a Tikhonov-based optimization approach (Method B).
  • To assess the feasibility of imaging myocardial activation time maps using MCG data from a patient with ventricular tachycardia.

Main Methods:

Related Experiment Videos

  • An iterative algorithm employing a general regularization scheme for nonlinear ill-posed problems in Hilbert scales was utilized (Method A).
  • Method A was compared with a Tikhonov-based second-order optimization routine for nonlinear ill-posed problems (Method B).
  • The algorithms were applied to magnetocardiographic recordings from a patient with idiopathic ventricular tachycardia, analyzing both sinus rhythm and ventricular extrasystolic beats.

Main Results:

  • Method A exhibited favorable computational performance.
  • The regularization parameter (lambda) determination scheme in Method A was found to be simpler than that in Method B.
  • Successful application of the formulation to patient-specific MCG data was demonstrated.

Conclusions:

  • The iterative regularization algorithm (Method A) provides an effective and computationally efficient approach for myocardial activation time mapping using MCG.
  • Method A offers advantages in regularization parameter selection, simplifying its application.
  • This study validates the potential of advanced inverse problem solutions for clinical applications in cardiac electrophysiology.