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

5.1K
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...
5.1K
Aliasing01:18

Aliasing

133
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
133

You might also read

Related Articles

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

Sort by
Same author

<i>d</i>-Band Engineering of Layered (Fe<sub>1-<i>x</i></sub>Ni<i><sub>x</sub></i>)<sub>3</sub>GaTe<sub>2</sub> for Enhanced Alkaline Hydrogen Evolution by Ni-Substitutional Doping.

Nanomaterials (Basel, Switzerland)·2026
Same author

Epidemiological characteristics of human parainfluenza virus infection in hospitalized children: a single-center study.

Frontiers in public health·2026
Same author

A comparative analysis of CFD and LBM for investigating the effects of endothelial glycocalyx on the bifurcating blood flow.

Microvascular research·2026
Same author

Colloidal Structure Engineering of Perovskite Precursor for Efficient Pure Blue LEDs with High Chlorine Content.

ACS applied materials & interfaces·2026
Same author

Integrated GRM-based efficient multi-performance prediction method for reconfigurable Fabry-Perot antennas.

Scientific reports·2026
Same author

PWD: Prior-Aware Wavelet Diffusion for Efficient Dental Limited-Angle CT Reconstruction.

IEEE transactions on medical imaging·2026

Related Experiment Video

Updated: Jun 29, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.2K

Correlated and Multi-Frequency Diffusion Modeling for Highly Under-Sampled MRI Reconstruction.

Yu Guan, Chuanming Yu, Zhuoxu Cui

    IEEE Transactions on Medical Imaging
    |March 25, 2024
    PubMed
    Summary

    This study introduces a Correlated and Multi-frequency Diffusion Model (CM-DM) to improve MRI reconstruction accuracy. The novel method enhances fine details and textural information in highly under-sampled images, achieving superior results.

    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

    19.5K
    Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
    15:48

    Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

    Published on: December 15, 2014

    22.5K

    Related Experiment Videos

    Last Updated: Jun 29, 2025

    Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
    17:06

    Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

    Published on: November 8, 2012

    26.2K
    Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
    09:30

    Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

    Published on: December 18, 2016

    19.5K
    Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
    15:48

    Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

    Published on: December 15, 2014

    22.5K

    Area of Science:

    • Medical Imaging
    • Image Reconstruction
    • Artificial Intelligence in Medicine

    Background:

    • Existing Magnetic Resonance Imaging (MRI) reconstruction methods struggle with accuracy for diagnostically significant tissues, particularly in highly under-sampled images.
    • Current approaches often reconstruct the entire image without preserving fine details and high-frequency content.
    • There is a need for methods that effectively mine and integrate high-frequency information for improved MRI reconstruction.

    Purpose of the Study:

    • To develop an innovative principle for highly under-sampled MRI reconstruction that enhances accuracy and preserves fine details.
    • To explore effective combinations of methods for mining high-frequency information.
    • To achieve superior reconstruction accuracy by maximizing the joint utilization of different approaches.

    Main Methods:

    • Introduction of the Correlated and Multi-frequency Diffusion Model (CM-DM) for highly under-sampled MRI reconstruction.
    • Formulation of a correlated and multi-frequency prior through different high-frequency operators within the diffusion process.
    • Utilizing the multi-frequency prior to constrain the noise term in the frequency domain, accelerating diffusion process convergence.

    Main Results:

    • The proposed CM-DM method demonstrated superior reconstruction accuracy in experimental results.
    • Achieved a notable enhancement of approximately 2dB in Peak Signal-to-Noise Ratio (PSNR) compared to state-of-the-art methods.
    • Successfully preserved high-frequency content and fine textural details in reconstructed MRI images.

    Conclusions:

    • The Correlated and Multi-frequency Diffusion Model (CM-DM) offers an effective solution for highly under-sampled MRI reconstruction.
    • The method's ability to combine and leverage multi-frequency information significantly improves reconstruction fidelity.
    • CM-DM represents a substantial advancement in achieving diagnostically accurate MRI reconstructions from limited data.