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

7.5K
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...
7.5K

You might also read

Related Articles

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

Sort by
Same author

Zero-Shot Self-Supervised Learning of Single Breath-Hold Magnetic Resonance Cholangiopancreatography (MRCP) Reconstruction.

Magnetic resonance in medicine·2026
Same author

BART Streams: Real-Time Reconstruction Using a Modular Framework for Pipeline Processing.

Magnetic resonance in medicine·2026
Same author

Isocaloric liquid and solid meals induce comparable postprandial gastric motility: Implications for oral drug delivery assessed by real-time MRI.

International journal of pharmaceutics: X·2026
Same author

Fast Real-Time Cardiac MRI: a Review of Current Techniques and Future Directions.

Investigative magnetic resonance imaging·2026
Same author

Fast and Robust Diffusion Posterior Sampling for MR Image Reconstruction Using the Preconditioned Unadjusted Langevin Algorithm.

Magnetic resonance in medicine·2026
Same author

FENCE: Flexible Electric Noise Reduction Endo-Shield for the Suppression of Electromagnetic Interference in Low-Field MRI.

NMR in biomedicine·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026
Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: Apr 27, 2026

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
09:59

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

Published on: September 16, 2017

16.7K

Fast T2 mapping with improved accuracy using undersampled spin-echo MRI and model-based reconstructions with a

Tilman J Sumpf, Andreas Petrovic, Martin Uecker

    IEEE Transactions on Medical Imaging
    |July 3, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new model-based method for faster and more accurate T2 mapping in magnetic resonance imaging (MRI). The technique improves T2 value accuracy from undersampled MRI data, enabling quicker scans.

    More Related Videos

    Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
    09:30

    Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

    Published on: December 18, 2016

    18.6K
    Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration
    05:30

    Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration

    Published on: May 19, 2023

    2.3K

    Related Experiment Videos

    Last Updated: Apr 27, 2026

    A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
    09:59

    A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

    Published on: September 16, 2017

    16.7K
    Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
    09:30

    Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

    Published on: December 18, 2016

    18.6K
    Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration
    05:30

    Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration

    Published on: May 19, 2023

    2.3K

    Area of Science:

    • Medical Imaging
    • Biophysics
    • Computational Science

    Background:

    • Accelerated magnetic resonance imaging (MRI) techniques are crucial for reducing scan times.
    • Accurate T2 mapping is essential for characterizing tissue properties in MRI.
    • Conventional methods for T2 mapping often require long acquisition times or are limited in accuracy.

    Purpose of the Study:

    • To develop and validate a model-based reconstruction technique for accelerated T2 mapping using undersampled Cartesian spin-echo MRI data.
    • To improve the accuracy of T2 quantification compared to conventional methods.
    • To enable significant acceleration of T2 mapping acquisition without compromising accuracy.

    Main Methods:

    • A novel signal model for T2 relaxation was developed, incorporating indirect echo contributions.
    • An iterative nonlinear inverse reconstruction algorithm was employed to directly estimate spin-density and T2 maps from undersampled data.
    • The technique was validated using simulated data, phantom studies, and human brain MRI at 3 Tesla.

    Main Results:

    • The proposed method demonstrated improved accuracy in T2 value estimation compared to conventional mono-exponential fitting.
    • The technique successfully allowed for retrospective undersampling factors of at least 6.
    • Limitations for very long T2 relaxation times were identified and a potential solution using gradient dampening was proposed.

    Conclusions:

    • The model-based reconstruction technique offers a promising approach for accelerated and accurate T2 mapping in MRI.
    • This method has the potential to significantly reduce MRI scan times while maintaining diagnostic quality.
    • The availability of the source code facilitates further research and clinical implementation.