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

8.8K
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...
8.8K
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

12.1K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
12.1K
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

192
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,...
192
Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

267
Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
267
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

715
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
715

You might also read

Related Articles

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

Sort by
Same author

MRI-Based Radiomics Ensemble Model for Predicting Radiation Necrosis in Brain Metastasis Patients Treated with Stereotactic Radiosurgery and Immunotherapy.

Cancers·2025
Same author

Extrapulmonary Small Cell Carcinoma: A Single-Institution Review of Brain Metastases, Treatment Paradigms, and Patient Outcomes.

Cureus·2025
Same author

Standardized reporting for Head CT Scans in patients suspected of traumatic brain injury (TBI): An international expert endeavor.

Neuroradiology·2024
Same author

Perineural Invasion As the Sole Pathologic Risk Factor After Surgical Resection for Head and Neck Squamous Cell Carcinoma.

Cureus·2021
Same author

Cisplatin/5-Fluorouracil (5-FU) Versus Carboplatin/Paclitaxel Chemoradiotherapy as Definitive or Pre-Operative Treatment of Esophageal Cancer.

Cureus·2021
Same author

Malignant Pericardial Mesothelioma Treated Using Volumetric Modulated Arc Therapy With a Simultaneous Integrated Boost.

Advances in radiation oncology·2021
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026
Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: Dec 28, 2025

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
08:51

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla

Published on: February 19, 2021

9.6K

Multi-Contrast Super-Resolution MRI Through a Progressive Network.

Qing Lyu, Hongming Shan, Cole Steber

    IEEE Transactions on Medical Imaging
    |February 23, 2020
    PubMed
    Summary
    This summary is machine-generated.

    New neural networks enhance Magnetic Resonance Imaging (MRI) resolution using multiple image contrasts. These advanced methods improve image quality and outperform existing techniques for clearer medical diagnostics and research.

    More Related Videos

    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

    26.8K
    High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
    10:06

    High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

    Published on: May 10, 2012

    13.3K

    Related Experiment Videos

    Last Updated: Dec 28, 2025

    Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
    08:51

    Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla

    Published on: February 19, 2021

    9.6K
    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

    26.8K
    High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
    10:06

    High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

    Published on: May 10, 2012

    13.3K

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Biomedical Engineering

    Background:

    • Magnetic Resonance Imaging (MRI) is crucial for medical diagnostics, offering superior soft-tissue contrast compared to CT and nuclear imaging.
    • Existing super-resolution methods often use single-contrast MRI, limiting their potential to leverage complementary information across different contrasts.
    • Multi-contrast MRI super-resolution aims to synergize information from various contrasts for enhanced image detail and diagnostic accuracy.

    Purpose of the Study:

    • To develop novel neural network architectures for multi-contrast MRI super-resolution.
    • To investigate the effectiveness of integrating multi-contrast information in high-level feature spaces.
    • To compare the performance of progressive versus non-progressive network designs for varying degrees of down-sampling.

    Main Methods:

    • Proposed a one-level non-progressive neural network for low up-sampling and a two-level progressive network for high up-sampling.
    • Networks integrate multi-contrast information in a high-level feature space.
    • Optimized imaging performance using a composite loss function including MSE, adversarial, perceptual, and textural losses.

    Main Results:

    • The proposed networks generated high-quality MRI super-resolution images, outperforming existing multi-contrast methods in structural similarity and peak signal-to-noise ratio.
    • Integrating multi-contrast information in a high-level feature space yielded significantly better results than low-level pixel space integration.
    • The progressive network achieved superior super-resolution image quality compared to the non-progressive network, especially for highly down-sampled images.

    Conclusions:

    • The developed neural networks effectively enhance multi-contrast MRI super-resolution.
    • High-level feature fusion and progressive network designs are key to achieving superior performance in MRI super-resolution.
    • These advancements hold promise for improved diagnostic accuracy and research applications using MRI.