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

357
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,...
357
Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

10.3K
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...
10.3K

You might also read

Related Articles

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

Sort by
Same author

Re: De Luca et al. Primary Noncontrast Magnetic Resonance Imaging for Prostate Cancer Screening: A Randomized Clinical Trial (PROSA). Eur Urol 2026;89:246-57.

European urology·2026
Same author

MRI-based Response Assessment of Neoadjuvant Systemic Immunotherapy in Muscle-invasive Bladder Cancer: An Analysis of Inter-radiologist Variability and Diagnostic Accuracy in three Prospective Clinical Trials.

European urology oncology·2026
Same author

Endotype-Guided Imaging in Chronic Rhinosinusitis: HRCT/CBCT and MRI Metrics, Structured Reporting, and Radiomics-A Systematic Review.

Medical sciences (Basel, Switzerland)·2026
Same author

Reply to Andrew R. A. Godtman et al.'s Letter to the Editor re: The PROSA Trial: Some Prosaic Comments. Eur Urol. In press. http://dx.doi.org/10.1016/j.eururo.2026.03.036.

European urology·2026
Same author

Digital twin technologies in prostate cancer as a frontier for precision medicine.

European radiology experimental·2026
Same author

Robot-assisted anterior abdomino-vaginal mesh suspension for stress urinary incontinence associated with anterior compartment pelvic organ prolapse: technique, imaging workflow, and 12-month pilot outcomes.

Frontiers in surgery·2026

Related Experiment Video

Updated: Mar 27, 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

1.9K

Integrating Artificial Intelligence into Prostate MR Imaging: Technical Foundations, Clinical Applications, and

Emanuele Messina1, Simone Novelli2,3, Ludovica Laschena1

  • 1Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy.

Magnetic Resonance in Medical Sciences : MRMS : an Official Journal of Japan Society of Magnetic Resonance in Medicine
|March 26, 2026
PubMed
Summary

Artificial intelligence (AI) enhances prostate MRI for better cancer detection and efficiency. While promising, AI is a tool to support radiologists, not replace them, requiring careful implementation and validation.

Keywords:
artificial inteligenceimage qualitymachine learningprostate cancerprostate magnetic resonance imaging

More Related Videos

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

1.3K
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

22.7K

Related Experiment Videos

Last Updated: Mar 27, 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

1.9K
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

1.3K
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

22.7K

Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Prostate MRI is crucial for cancer diagnosis but is technically demanding and prone to variability.
  • Abbreviated MRI protocols, like non-contrast (biparametric) MRI, increase the need for efficiency and consistency.

Purpose of the Study:

  • To provide a comprehensive overview of AI integration in prostate MRI.
  • To review AI's technical foundations, clinical applications, and workflow implications.
  • To discuss challenges and future directions for AI in prostate MRI.

Main Methods:

  • Non-systematic narrative review of AI in prostate MRI.
  • Summarized machine learning and deep learning concepts relevant to prostate imaging.
  • Reviewed evidence for AI in image quality, segmentation, detection, and risk stratification.

Main Results:

  • AI shows promise in enhancing image quality, segmentation, lesion detection, and risk stratification in prostate MRI.
  • Human-AI collaboration models and AI's role in supporting equivocal findings are discussed.
  • Commercial AI tools and their impact on reporting, training, and standardization are examined.

Conclusions:

  • AI is a valuable decision-support tool for prostate MRI, complementing radiologists' expertise.
  • Challenges include validation, generalizability, and ethical/regulatory considerations.
  • Successful AI implementation requires thoughtful integration, robust validation, and user training for improved prostate cancer care.