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

10.3K
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...
10.3K

You might also read

Related Articles

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

Sort by
Same author

Self-Cooperative RNA Vaccine Mitigates Dendritic Cell-Mediated Acquired Immune Resistance to Potentiate Cell Therapy for Solid Tumors.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Design of tunable topological valley photonic crystal filter for arbitrary wavelength notch filtering.

Optics express·2026
Same author

Analysis of end-stage renal disease mediated by cuproptosis-related genes.

Clinical nephrology·2026
Same author

Premature Aortic Stiffness in Relation to Cerebral Small Vessel Disease, Cognitive Decline, Major Cardiovascular Events and Mortality in Dialysis.

American journal of nephrology·2026
Same author

Erratum: "Thermal and chemical control of emission and excited-state dynamics in non-(TMS)3P-derived InP quantum dots" [J. Chem. Phys. 164, 144702 (2026)].

The Journal of chemical physics·2026
Same author

Comparative efficacy of CDK4/6 inhibitors palbociclib, ribociclib, and abemaciclib in HR<sup>+</sup>/HER2<sup>-</sup> advanced breast cancer.

American journal of cancer research·2026

Related Experiment Video

Updated: Mar 25, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

264

Enhanced muscle MRI using deep learning: shorter acquisition time with improved image quality.

Peilin Fan1, Zaizhu Zhang1, Bo Hou1

  • 1Radiology, Peking Union Medical College Hospital, Beijing, China.

Peerj
|March 24, 2026
PubMed
Summary

Deep learning (DL) reconstruction combined with fat-suppressed turbo spin-echo T2-weighted imaging (TSE T2WI) significantly improves muscle MRI quality and reduces scan times. This advanced technique shows promise for routine clinical use in musculoskeletal imaging.

Keywords:
Artificial intelligenceDeep learningDiagnostic imagingMagnetic resonance imagingThigh

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

20.2K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.7K

Related Experiment Videos

Last Updated: Mar 25, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

264
Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

20.2K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.7K

Area of Science:

  • Musculoskeletal Imaging
  • Medical Physics
  • Radiology

Background:

  • Muscle magnetic resonance imaging (MRI) traditionally involves lengthy acquisition times.
  • Fat-suppressed turbo spin-echo T2-weighted imaging (TSE T2WI) is crucial for visualizing muscle pathology.
  • Deep learning (DL) offers potential for accelerating MRI acquisition and reconstruction.

Purpose of the Study:

  • To evaluate the effectiveness of DL reconstruction for TSE T2WI in muscle MRI.
  • To assess the impact of DL on image quality and acquisition time.
  • To compare DL-reconstructed TSE T2WI (TSEDL) with standard TSE T2WI.

Main Methods:

  • Prospective study involving 98 controls and 33 patients undergoing bilateral thigh MRI at 3T.
  • Comparison of standard fat-suppressed TSE T2WI with DL-reconstructed TSE T2WI (TSEDL).
  • Quantitative analysis of noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR); qualitative assessment by two radiologists.

Main Results:

  • TSEDL acquisition time was significantly reduced (1 min 33s vs. 2 min 11s).
  • TSEDL demonstrated significantly lower noise and higher SNR and CNR compared to standard TSE (p < 0.05).
  • Qualitative analysis revealed superior image quality, better anatomical visualization, and higher diagnostic confidence with TSEDL (p < 0.01).

Conclusions:

  • DL reconstruction combined with fat-suppressed TSE T2WI significantly enhances muscle MRI quality.
  • This method substantially reduces MRI acquisition time.
  • DL-based reconstruction holds potential for routine clinical application in musculoskeletal imaging.