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

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

214
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,...
214
Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

8.9K
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...
8.9K

You might also read

Related Articles

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

Sort by
Same author

Effects of Computed Tomography Technical Parameters on Body-Composition Analysis.

Korean journal of radiology·2025
Same author

ProMUS-NET: Artificial intelligence detects more prostate cancer than urologists on micro-ultrasonography.

BJU international·2025
Same author

Fast MRI Techniques of the Liver and Pancreaticobiliary Tract: Overview and Application.

Journal of the Korean Society of Radiology·2025
Same author

Conversion of Mixed-Language Free-Text CT Reports of Pancreatic Cancer to National Comprehensive Cancer Network Structured Reporting Templates by Using GPT-4.

Korean journal of radiology·2025
Same author

Performance of GPT-4 Turbo and GPT-4o in Korean Society of Radiology In-Training Examinations.

Korean journal of radiology·2025
Same author

Deep Learning-Accelerated Non-Contrast Abbreviated Liver MRI for Detecting Malignant Focal Hepatic Lesions: Dual-Center Validation.

Korean journal of radiology·2025
Same journal

Additional Dose Reduction Potential of Vendor-Agnostic Deep Learning Model: A Phantom Study.

Journal of the Korean Society of Radiology·2026
Same journal

Techniques and Clinical Outcomes of Catheter-Directed Sclerotherapy Using Ethanol for Ovarian Endometriomas.

Journal of the Korean Society of Radiology·2026
Same journal

Primary Gastric Leiomyosarcoma Presenting as an Endoscopic Diagnostic Pitfall: A Case Report.

Journal of the Korean Society of Radiology·2026
Same journal

Breast Imaging Reporting and Data System v2025: Key Updates in Mammography.

Journal of the Korean Society of Radiology·2026
Same journal

Breast Imaging Reporting and Data System v2025: Key Updates in MRI.

Journal of the Korean Society of Radiology·2026
Same journal

Massive Psoas Muscle Bleeding from the Contralateral Lumbar Artery Obscured by a Lumbar Vertebra Fracture: A Case Report.

Journal of the Korean Society of Radiology·2026
See all related articles

Related Experiment Video

Updated: Jan 8, 2026

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

1.1K

AI in Prostate MRI: A Task-Based Review.

Moon Hyung Choi

    Journal of the Korean Society of Radiology
    |December 19, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Artificial intelligence (AI) shows promise in supporting prostate magnetic resonance imaging (MRI) interpretation for cancer detection, staging, and monitoring. Radiologists should understand AI

    Keywords:
    Artificial IntelligenceDeep LearningDiagnosisMagnetic Resonance ImagingProstate

    More Related Videos

    Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
    09:11

    Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

    Published on: April 9, 2019

    22.2K
    MR Molecular Imaging of Prostate Cancer with a Small Molecular CLT1 Peptide Targeted Contrast Agent
    06:54

    MR Molecular Imaging of Prostate Cancer with a Small Molecular CLT1 Peptide Targeted Contrast Agent

    Published on: September 3, 2013

    11.7K

    Related Experiment Videos

    Last Updated: Jan 8, 2026

    A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
    06:08

    A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

    Published on: March 21, 2025

    1.1K
    Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
    09:11

    Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

    Published on: April 9, 2019

    22.2K
    MR Molecular Imaging of Prostate Cancer with a Small Molecular CLT1 Peptide Targeted Contrast Agent
    06:54

    MR Molecular Imaging of Prostate Cancer with a Small Molecular CLT1 Peptide Targeted Contrast Agent

    Published on: September 3, 2013

    11.7K

    Area of Science:

    • Radiology
    • Artificial Intelligence
    • Oncology

    Background:

    • Prostate MRI is crucial for prostate cancer management.
    • Increasing demand for efficient image interpretation drives AI adoption.

    Purpose of the Study:

    • To review AI applications in prostate MRI across various clinical tasks.
    • To highlight AI's potential to support radiologists.

    Main Methods:

    • Task-based review of AI applications in prostate MRI.
    • Focus on deep learning algorithms.
    • Analysis of AI in segmentation, detection, staging, monitoring, and quality assessment.

    Main Results:

    • AI demonstrates promising results in key prostate MRI tasks.
    • Deep learning is a key driver of AI advancements.
    • Limited commercial AI tools are currently available.

    Conclusions:

    • AI offers significant potential to enhance prostate MRI interpretation.
    • Radiologists need to understand AI's role in clinical practice.
    • Future advancements in AI for prostate MRI are expected.