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

7.6K
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...
7.6K
NMR Spectrometers: Resolution and Error Correction01:14

NMR Spectrometers: Resolution and Error Correction

999
When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
999

You might also read

Related Articles

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

Sort by
Same author

Okanin alleviates osteoarthritis by suppressing oxidative stress and pyroptosis via Nrf2/HO-1 activation.

European journal of pharmacology·2026
Same author

Magnetic Resonance Spectroscopy Deep Learning with Magnetic Resonance Background Generator Enables In Vivo Metabolite Quantification of Hepatic Encephalopathy.

IEEE transactions on bio-medical engineering·2026
Same author

Cable bacteria drive electrochemical coupling and elemental cycling in rhizosphere: A review.

Ying yong sheng tai xue bao = The journal of applied ecology·2026
Same author

Atomically confined insertion for 2D strain and polarization engineered GaN electronics.

Nature communications·2026
Same author

A spatiotemporal dependency-aware lightweight CNN-ViT network for 3D MRF with a balanced acceleration strategy.

Medical image analysis·2026
Same author

Morphological feature remodeling of intracranial arteries in the context of inflammation and HIV-associated cognitive impairment.

medRxiv : the preprint server for health sciences·2026

Related Experiment Video

Updated: May 6, 2026

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

1.6K

Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator.

Xiaobo Qu1, Yingkun Hou2, Fan Lam3

  • 1Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China.

Medical Image Analysis
|November 2, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel patch-based nonlocal operator (PANO) for compressed sensing MRI (CS-MRI). PANO enhances image reconstruction by improving sparse representation, leading to reduced errors and better visual quality in MRI scans.

Keywords:
Compressed sensingImage reconstructionMagnetic resonance imagingNonlocal operatorSparsity

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
Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods
05:07

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

Published on: September 6, 2024

956

Related Experiment Videos

Last Updated: May 6, 2026

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

1.6K
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
Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods
05:07

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

Published on: September 6, 2024

956

Area of Science:

  • Medical Imaging
  • Magnetic Resonance Imaging (MRI)
  • Image Reconstruction

Background:

  • Compressed Sensing MRI (CS-MRI) accelerates data acquisition.
  • Image reconstruction quality in CS-MRI relies on sparsity.
  • Traditional sparsifying transforms may not sufficiently represent MRI data.

Purpose of the Study:

  • To develop a novel patch-based nonlocal operator (PANO) for improved MRI sparsity.
  • To enhance the sparse representation of magnetic resonance images.
  • To reduce image reconstruction error and improve visual quality in CS-MRI.

Main Methods:

  • Designed a patch-based nonlocal operator (PANO) leveraging image patch similarity.
  • Formulated a general approach to balance patch sparsity and data consistency.
  • Incorporated prior information from undersampled data or other contrasts for optimized sparsity.

Main Results:

  • PANO achieves a more effective sparse representation for similar image patches.
  • The proposed method demonstrates lower reconstruction error compared to conventional CS-MRI.
  • Higher visual quality of reconstructed images was observed with the PANO method.

Conclusions:

  • PANO offers a superior method for sparsifying MRI data, enhancing CS-MRI performance.
  • The approach allows for flexible incorporation of prior information to optimize image reconstruction.
  • This technique significantly improves the efficiency and quality of compressed sensing MRI.