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

7.9K
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...
7.9K
Applications Of NMR In Biology01:25

Applications Of NMR In Biology

4.0K
Nuclear magnetic resonance (NMR) spectroscopy is a very valuable analytical technique for researchers. It has been used for more than 50 years as an analytical tool. F. Bloch and E. Purcell formulated NMR in 1946 and won the 1952 Nobel Prize in Physics  for their work. Biological macromolecules such as proteins, nucleic acids, lipids, and organic molecules including pharmaceutical compounds, can be studied using this versatile tool that exploits the magnetic properties of certain nuclei.
4.0K
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

70
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,...
70
Brain Imaging01:14

Brain Imaging

376
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
376
Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

155
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,...
155
Nuclear Magnetic Resonance (NMR): Overview01:07

Nuclear Magnetic Resonance (NMR): Overview

4.7K
Nuclear magnetic resonance (NMR) is a phenomenon exhibited by certain nuclei that can absorb characteristic radio frequency radiation under certain conditions. NMR has been extensively applied in molecular spectroscopy and medical diagnostic imaging. In both these applications, the molecule or subject under study is placed in a magnetic field and irradiated with radio frequency energy.
NMR spectroscopy generates a spectrum where the characteristic absorption frequencies of the sample are...
4.7K

You might also read

Related Articles

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

Sort by
Same author

Vessel Wall Imaging in 1.5 T MRI Using Deep Learning Reconstruction: Prospective Evaluation of Interchangeability With Standard 3 T MRI.

Investigative radiology·2026
Same author

Accelerated Deep-Learning-Based Image Reconstruction for 3D T2 Dark-Fluid in Imaging of Multiple Sclerosis.

Investigative radiology·2026
Same author

Impact of Deep-Learning Reconstruction on MRI Workflows: A Retrospective Analysis at a Large Academic Tertiary Center.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin·2026
Same author

Clinical Evaluation of Deep Learning-Reconstructed Postcontrast 3D T1-Weighted Volume Interpolated Breath-Hold Examination (VIBE) Compared with Standard VIBE for Detection of Internal Auditory Canal Lesions.

AJNR. American journal of neuroradiology·2026
Same author

Photon Counting CT of the Head: Retrospective Analysis of Image Quality and Dose in Comparison to Energy Integrating Detector CT.

Academic radiology·2026
Same author

Blood pressure management in patients receiving rescue stenting after failed endovascular treatment in large vessel occlusion acute ischaemic stroke: a multicentre registry.

European stroke journal·2026

Related Experiment Video

Updated: Oct 9, 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

9.3K

Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present?

Sebastian Gassenmaier1, Thomas Küstner2, Dominik Nickel3

  • 1Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, 72076 Tuebingen, Germany.

Diagnostics (Basel, Switzerland)
|December 24, 2021
PubMed
Summary

Deep learning is revolutionizing radiology, particularly in magnetic resonance imaging reconstruction. These advanced AI techniques promise faster scans, improved image quality, and workflow acceleration, transforming clinical practice.

Keywords:
DLGREMRIMSKTSEdeep learningprostate MRI

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

19.7K
Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging
07:59

Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging

Published on: October 13, 2019

7.7K

Related Experiment Videos

Last Updated: Oct 9, 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

9.3K
Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

19.7K
Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging
07:59

Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging

Published on: October 13, 2019

7.7K

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Deep learning (DL) represents a significant advancement in radiology.
  • Its applications are poised to transform daily clinical practice in image acquisition and reporting.

Purpose of the Study:

  • To review current deep learning technologies, focusing on magnetic resonance (MR) image reconstruction.
  • To examine the technical principles, clinical translation, and literature on DL in MR image reconstruction and post-processing.

Main Methods:

  • Literature review of deep learning applications in magnetic resonance imaging reconstruction.
  • Analysis of technical principles and clinical translation of DL techniques.
  • Synthesis of current research on DL for MR image reconstruction and post-processing.

Main Results:

  • Deep learning is a major emerging force in radiology.
  • DL-based MR image reconstruction offers reduced acquisition times and enhanced image quality.
  • These techniques can accelerate workflows and potentially alleviate scanner availability issues.

Conclusions:

  • Deep learning technologies, especially in MR image reconstruction, hold immense promise for radiology.
  • The integration of DL is expected to permanently alter daily routines and workflows.
  • DL offers significant advantages in diagnostic support, efficiency, and image quality.