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 Video

Updated: Jun 4, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

A reference dataset for verifying numerical electrophysiological heart models.

Hans Koch1, Ralf-Dieter Bousseljot, Olaf Kosch

  • 1Physikalisch-Technische Bundesanstalt, Abbestr, 2-12, 10587 Berlin, Germany. hans.koch@ptb.de

Biomedical Engineering Online
|January 29, 2011
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

Loeffler's Endomyocarditis: Clinical characteristics and outcomes from a tertiary care centre registry.

International journal of cardiology·2026
Same author

Synthetically trained convolutional neural networks for time-resolved aortic segmentation of four-dimensional flow magnetic resonance imaging.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance·2026
Same author

Magnetic resonance-based computational modelling of healthy and prolapsing mitral valves to quantify the load transfer between the mitral apparatus and the ventricular myocardium.

Computer methods and programs in biomedicine·2025
Same author

Correlation of cavotricuspid isthmus dynamics with clinical parameters: insights from interventional cardiac magnetic resonance imaging.

European heart journal. Imaging methods and practice·2025
Same author

Splenic switch-off in three-dimensional adenosine stress cardiac magnetic resonance perfusion for differentiating true-negative from potentially false-negative studies identified by fractional flow reserve.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance·2025
Same author

Late gadolinium enhancement imaging for the prediction of ventricular tachycardia ablation outcome.

Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing·2025

A new dataset enables the evaluation of numerical heart models. Magnetic Resonance Imaging (MRI) data and biosignals like Body Surface Potential Maps (BSPM) and MagnetoCardioGraphy (MCG) allow model verification.

Area of Science:

  • Computational biology
  • Biomedical engineering
  • Cardiovascular research

Background:

  • Evaluating numerical heart models is challenging due to the lack of a standardized reference database.
  • This study aimed to create an exemplary dataset to facilitate model comparison.

Purpose of the Study:

  • To compile a comprehensive dataset for the evaluation and verification of numerical heart models.
  • To provide a common input (MRI data) and measured biosignals (BSPM, MCG) for model assessment.

Main Methods:

  • Magnetic Resonance Imaging (MRI) of the heart and torso.
  • Recording of Body Surface Potential Maps (BSPM) and MagnetoCardioGraphy (MCG) maps.
  • Simultaneous recording of BSPM and MCG from the same individuals post-MRI.

More Related Videos

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Related Experiment Videos

Last Updated: Jun 4, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Main Results:

  • A training dataset has been made publicly available.
  • Datasets for blind testing will be kept undisclosed to ensure objective model evaluation.

Conclusions:

  • MRI data can serve as a standardized input for diverse numerical heart models.
  • Model verification and comparison are achievable by contrasting measured biosignals with model-derived forward calculations.