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

5.4K
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...
5.4K
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

40
Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
40
Atomic Nuclei: Magnetic Resonance01:05

Atomic Nuclei: Magnetic Resonance

714
The number of nuclear spins aligned in the lower energy state is slightly greater than those in the higher energy state. In the presence of an external magnetic field, as the spins precess at the Larmor frequency, the excess population results in a net magnetization oriented along the z axis. When a pulse or a short burst of radio waves at the Larmor frequency is applied along the x axis, the coupling of frequencies causes resonance and flips the nuclear spins of the excess population from the...
714
NMR Spectrometers: Resolution and Error Correction01:14

NMR Spectrometers: Resolution and Error Correction

748
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...
748
2D NMR: Overview of Heteronuclear Correlation Techniques01:18

2D NMR: Overview of Heteronuclear Correlation Techniques

261
Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other...
261
Diamagnetic Shielding of Nuclei: Local Diamagnetic Current01:14

Diamagnetic Shielding of Nuclei: Local Diamagnetic Current

918
An applied magnetic field causes the electrons present in the molecule to circulate, setting up a local diamagnetic current within the molecule. The local diamagnetic current arising from circulating sigma-bonding electrons induces a magnetic field, Blocal that opposes the applied magnetic field, B0. The effective magnetic field experienced by these nuclei is given by the difference between the applied and local magnetic fields in a phenomenon called local diamagnetic shielding. Essentially,...
918

You might also read

Related Articles

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

Sort by
Same author

Dynamic balance status and ankle mobility in adolescent basketball players with a history of multiple lateral ankle sprains.

Clinical biomechanics (Bristol, Avon)·2026
Same author

The extrapolated body center of mass predicts subsequent foot placement choice during dynamic single-leg landings.

Journal of biomechanics·2026
Same author

Exceptional Long-Term Survival after Resection of Metachronous Liver and Lung Metastases Following Surgery for Pancreatic Cancer: A Case Report.

Surgical case reports·2026
Same author

Adjunctive posterior wall isolation for persistent and long-standing persistent atrial fibrillation: the CORNERSTONE AF trial.

European heart journal·2026
Same author

Age-Dependent Effects of Adrenomedullin on Muscle Fiber Composition, Angiogenesis, and Muscle Satellite Cells Maintenance.

Geriatrics & gerontology international·2026
Same author

Mild hyperbaric oxygen enhanced the regeneration of the tibialis anterior muscle after the cardiotoxin-induced muscle injury.

Biochemistry and biophysics reports·2026

Related Experiment Video

Updated: Aug 7, 2025

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
09:55

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases

Published on: January 5, 2024

1.3K

Denoising Using Noise2Void for Low-Field Magnetic Resonance Imaging: A Phantom Study.

Shinya Kojima1, Toshimune Ito1, Tatsuya Hayashi1

  • 1Department of Radiological Technology, Faculty of Medical Technology, Teikyo University, Itabashi-ku, Tokyo, Japan.

Journal of Medical Physics
|March 13, 2023
PubMed
Summary

Noise2Void (N2V) effectively reduces noise in low-field magnetic resonance imaging (MRI). This deep learning method significantly improves image quality metrics like signal-to-noise ratio (SNR) and structural similarity (SSIM).

Keywords:
Denoising convolutional neural networkNoise2Voidlow-field magnetic resonance imaging

More Related Videos

Fat-Water Phantoms for Magnetic Resonance Imaging Validation: A Flexible and Scalable Protocol
07:59

Fat-Water Phantoms for Magnetic Resonance Imaging Validation: A Flexible and Scalable Protocol

Published on: September 7, 2018

11.4K
Use of a Multi-compartment Dynamic Single Enzyme Phantom for Studies of Hyperpolarized Magnetic Resonance Agents
08:59

Use of a Multi-compartment Dynamic Single Enzyme Phantom for Studies of Hyperpolarized Magnetic Resonance Agents

Published on: April 15, 2016

6.9K

Related Experiment Videos

Last Updated: Aug 7, 2025

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
09:55

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases

Published on: January 5, 2024

1.3K
Fat-Water Phantoms for Magnetic Resonance Imaging Validation: A Flexible and Scalable Protocol
07:59

Fat-Water Phantoms for Magnetic Resonance Imaging Validation: A Flexible and Scalable Protocol

Published on: September 7, 2018

11.4K
Use of a Multi-compartment Dynamic Single Enzyme Phantom for Studies of Hyperpolarized Magnetic Resonance Agents
08:59

Use of a Multi-compartment Dynamic Single Enzyme Phantom for Studies of Hyperpolarized Magnetic Resonance Agents

Published on: April 15, 2016

6.9K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence

Background:

  • Low-field magnetic resonance imaging (MRI) often suffers from significant noise, limiting image quality and diagnostic potential.
  • Traditional denoising methods may require perfectly clean reference images, which are difficult to obtain in low-field MRI.
  • Noise2Void (N2V) is a deep learning approach that enables model training without requiring noiseless ground truth data.

Purpose of the Study:

  • To evaluate the effectiveness of Noise2Void (N2V) for noise reduction in low-field MRI.
  • To demonstrate the validity and performance of N2V in enhancing low-field MRI image quality.
  • To quantify the impact of N2V on key image quality metrics.

Main Methods:

  • A kiwi fruit was scanned using a 0.35 Tesla MRI system.
  • Images were processed using the Noise2Void (N2V) deep learning denoising technique.
  • Image quality was assessed using quantitative metrics: Structural Similarity Index (SSIM), Signal-to-Noise Ratio (SNR), and Contrast Ratio (CR).
  • Visual assessment of noise levels and image sharpness was performed pre- and post-denoising.

Main Results:

  • Noise2Void (N2V) significantly improved both SSIM and SNR in low-field MRI images (P < 0.05).
  • Contrast Ratio (CR) remained unchanged after N2V denoising.
  • Visual assessment indicated reduced noise and improved sharpness in post-denoising images, particularly when initial SNR was low.

Conclusions:

  • Noise2Void (N2V) is a highly effective technique for noise reduction in low-field MRI.
  • N2V demonstrates significant potential as a valuable tool for improving low-field MRI image quality.
  • The method shows promise for enhancing diagnostic capabilities in resource-limited MRI settings.