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

8.7K
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...
8.7K
Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

235
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,...
235
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

648
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...
648
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

163
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,...
163

You might also read

Related Articles

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

Sort by
Same author

Complex Deep Neural Networks for Denoising Ultra-Fast Submillimeter T2*-weighted Imaging and Quantitative Susceptibility Mapping.

European journal of radiology artificial intelligence·2026
Same author

A Porcine Model of Intervertebral Disc Injury Recapitulates Human Discogenic Pain Via Notochordal Cell Loss and Pain-Inducing Nucleus Pulposus Cell Emergence.

JOR spine·2026
Same author

Coffee Consumption and Improved Liver Outcomes: Clinical, Imaging, and Proteomic Evidence From the UK Biobank.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association·2026
Same author

The "Brain's Traffic Map" Reveals Neural Pathways Linked to Coronary Microvascular Dysfunction in Women.

Brain and behavior·2026
Same author

Refined liver MRI-derived cT1 thresholds capturing hepatic fat fraction enhance mortality risk prediction.

JHEP reports : innovation in hepatology·2026
Same author

Hierarchical organ aging signatures from routine abdominal CT add incremental disease risk stratification beyond blood biomarkers.

medRxiv : the preprint server for health sciences·2026

Related Experiment Video

Updated: Dec 8, 2025

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

10.1K

Deriving new soft tissue contrasts from conventional MR images using deep learning.

Yan Wu1, Debiao Li2, Lei Xing1

  • 1Department of Radiation Oncology, Stanford University, Stanford, CA, United States of America.

Magnetic Resonance Imaging
|September 21, 2020
PubMed
Summary

This study introduces a deep learning method to generate diverse soft tissue contrasts from standard MRI scans. This approach enhances MRI versatility without requiring extra imaging time or specialized equipment.

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.2K
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

937

Related Experiment Videos

Last Updated: Dec 8, 2025

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

10.1K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.2K
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

937

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Magnetic Resonance Imaging (MRI) offers versatile soft tissue contrast, but this potential is often underutilized in clinical practice.
  • Conventional MRI protocols may not always capture the full spectrum of soft tissue information required for comprehensive analysis.
  • Developing methods to extract more contrast information from existing MRI data is crucial for advancing diagnostic capabilities.

Purpose of the Study:

  • To propose and validate a deep learning-based strategy for generating enhanced soft tissue contrasts from conventional MRI.
  • To demonstrate the ability to predict MR images with different pulse sequences and imaging parameters from standard acquisitions.
  • To improve the exploitation of MRI's inherent soft tissue contrast versatility.

Main Methods:

  • A self-attention convolutional neural network (CNN) model was developed and trained on retrospective MRI data.
  • The model learned to predict specific MRI contrasts (e.g., T1ρ-weighted images) from other standard sequences (e.g., T2-weighted, T1-weighted).
  • The method was validated by predicting variable flip angle images and assessing quantitative T1 maps derived from them.

Main Results:

  • High accuracy was achieved in generating qualitative MR images with novel contrasts.
  • Quantitative T1 maps derived from predicted images showed high accuracy, validating the method's precision.
  • The deep learning approach successfully derived diverse soft tissue contrasts from standard clinical MRI data.

Conclusions:

  • The proposed deep learning strategy effectively enhances soft tissue contrast in MRI without additional scans.
  • This method offers a versatile solution for obtaining richer diagnostic information from existing MRI data.
  • The approach can be broadly applied to normalize inconsistently acquired MR data for quantitative analysis and improve diagnostic insights.