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

7.3K
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...
7.3K
Divergence and Curl of Magnetic Field01:26

Divergence and Curl of Magnetic Field

3.2K
The magnetic field due to a volume current distribution given by the Biot–Savart Law can be expressed as follows:
3.2K
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

60
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,...
60
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.2K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
1.2K
Deconvolution01:20

Deconvolution

263
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
263
NMR Spectrometers: Resolution and Error Correction01:14

NMR Spectrometers: Resolution and Error Correction

784
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...
784

You might also read

Related Articles

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

Sort by
Same author

Non-Adiabatic Effects on Excited States of Vinylidene Observed with Slow Photoelectron Velocity-Map Imaging.

Journal of the American Chemical Society·2016
Same author

Targeting Heparin to Collagen within Extracellular Matrix Significantly Reduces Thrombogenicity and Improves Endothelialization of Decellularized Tissues.

Biomacromolecules·2016
Same author

Association between sleep duration and the prevalence of hypertension in an elderly rural population of China.

Sleep medicine·2016
Same author

Association between passive smoking and hypertension in Chinese non-smoking elderly women.

Hypertension research : official journal of the Japanese Society of Hypertension·2016
Same author

Morphine versus methylprednisolone or aminophylline for relieving dyspnea in patients with advanced cancer in China: a retrospective study.

SpringerPlus·2016
Same author

Expression of Rab1A is upregulated in human lung cancer and associated with tumor size and T stage.

Aging·2016

Related Experiment Video

Updated: Sep 18, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.6K

3D Magnetic resonance image denoising using nonlocal and nonconvex tensor train regularization.

Li Wang1,2, Yun Zhao3, Liang Zhao4

  • 1College of Physics, Chongqing University, Chongqing, China. childlife@163.com.

Medical & Biological Engineering & Computing
|June 25, 2025
PubMed
Summary

This study introduces a new MRI denoising method using tensor train decomposition and non-local self-similarity. The advanced technique effectively removes noise from 3D magnetic resonance images, improving image quality for medical diagnosis.

Keywords:
Magnetic resonance imagesNon-local self-similarityTensor train decompositionWeighted Schatten-p norm

More Related Videos

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping
09:55

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

1.1K
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.1K

Related Experiment Videos

Last Updated: Sep 18, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.6K
Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping
09:55

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

1.1K
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.1K

Area of Science:

  • Medical Imaging
  • Image Processing
  • Applied Mathematics

Background:

  • Magnetic resonance image (MRI) denoising is crucial for accurate medical diagnosis.
  • Low-rank tensor methods show promise but struggle with characterizing 3D MR image structures.
  • Existing methods face limitations due to imbalanced matricization and nuclear norm penalties.

Purpose of the Study:

  • To propose a novel framework for MRI denoising that overcomes limitations of existing low-rank tensor methods.
  • To enhance the characterization of internal structure information in 3D MR images.
  • To improve the accuracy and visual quality of denoised MR images.

Main Methods:

  • A novel framework combining non-local self-similarity and tensor train decomposition with balanced matricization.
  • Construction of a fourth-order tensor using non-local self-similarity.
  • Application of tensor train regularization with a weighted Schatten-p norm function.

Main Results:

  • The proposed method effectively denoises 3D MR images by considering structural correlations across dimensions.
  • It accounts for the importance of various singular values, leading to better noise removal.
  • Experimental results show competitive performance against state-of-the-art denoising filters.

Conclusions:

  • The novel framework offers a significant advancement in MRI denoising.
  • It provides superior noise removal for 3D MR images compared to existing methods.
  • The approach enhances both visual and quantitative aspects of image quality for clinical applications.