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 Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

You might also read

Related Articles

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

Sort by
Same author

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

Computer methods and programs in biomedicine·2026
Same author

Predicting Patient Status in Chronic Thromboembolic Pulmonary Hypertension Using a Biophysical Model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2023
Same author

Cerebrovascular super-resolution 4D Flow MRI - Sequential combination of resolution enhancement by deep learning and physics-informed image processing to non-invasively quantify intracranial velocity, flow, and relative pressure.

Medical image analysis·2023
Same author

The Physician and His Microscope.

Buffalo medical journal·2023
Same author

Herpes Facialis.

Buffalo medical journal·2023
Same author

Erysipelas.

Buffalo medical journal·2023

Related Experiment Video

Updated: Jun 28, 2026

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

Passive ventricular mechanics modelling using MRI of structure and function.

V Y Wang1, H I Lam, D B Ennis

  • 1Auckland Bioengineering Institute, University of Auckland, New Zealand. vicky.wang@auckland.ac.nz

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|November 6, 2008
PubMed
Summary
This summary is machine-generated.

A new finite element model estimates passive left ventricular (LV) stiffness using MRI data. This advances understanding of diastolic dysfunction in heart disease by analyzing LV mechanics.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 28, 2026

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

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

Area of Science:

  • Cardiovascular Physiology
  • Biomedical Engineering
  • Medical Imaging

Background:

  • Left ventricular (LV) diastolic impairment can arise from conditions like dilated cardiomyopathy and myocardial infarction.
  • LV remodeling alters passive mechanics due to changes in geometry and loading conditions.
  • Understanding passive ventricular mechanics is crucial for diagnosing and treating diastolic dysfunction.

Purpose of the Study:

  • To develop a finite element model of the left ventricle to investigate passive mechanics during diastole.
  • To integrate in vivo and ex vivo imaging and pressure data for a comprehensive cardiac model.
  • To estimate passive myocardial stiffness in the LV based on simulated diastolic mechanics.

Main Methods:

  • A canine LV finite element model was created using in vivo MRI tissue tagging and cavity pressure recordings.
  • Ex vivo diffusion tensor MRI (DTMRI) provided myocardial fiber orientation data.
  • Nonlinear finite element fitting and free form deformation techniques customized the model geometry and fiber orientations.
  • Simulations of diastolic LV mechanics were performed using synchronized pressure and tagging MRI data.

Main Results:

  • The model successfully integrated diverse physiological and mechanical data.
  • Diastolic LV mechanics were simulated, allowing for the estimation of passive myocardial stiffness.
  • The study provided insights into regional passive diastolic mechanics.

Conclusions:

  • This integrated physiological model enhances understanding of passive left ventricular mechanics.
  • It offers a pathway for individualized assessment of diastolic dysfunction.
  • The model aids in elucidating the structural basis of mechanical dysfunction in pathological cardiac conditions.