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Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

50
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,...
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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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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...
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Related Experiment Video

Updated: Sep 3, 2025

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

Published on: March 21, 2025

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Tasks for artificial intelligence in prostate MRI.

Mason J Belue1, Baris Turkbey2

  • 1Molecular Imaging Branch, National Cancer Institute, National Institutes of Health Bethesda, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, MD, 20892-1088, USA.

European Radiology Experimental
|July 30, 2022
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise in prostate cancer diagnostics, improving image quality, segmentation, and detection. Further multicenter studies are needed to confirm AI

Keywords:
Artificial intelligenceDeep learningMachine learningMagnetic resonance imagingProstatic neoplasms

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Prostate Cancer Diagnostics

Background:

  • Precision medicine and increased imaging availability drive AI adoption in prostate cancer diagnostics.
  • AI offers diverse applications across the diagnostic pathway, from image enhancement to lesion classification.

Purpose of the Study:

  • To review the current applications of AI in prostate cancer diagnostics.
  • To introduce AI paper quality metrics like CLAIM and FWCI.
  • To highlight top AI models for segmentation, detection, and classification.

Main Methods:

  • Narrative review of existing literature on AI in prostate cancer diagnostics.
  • Discussion of AI applications in image quality, segmentation, detection, and classification.
  • Introduction of emerging AI evaluation metrics.

Main Results:

  • Numerous studies demonstrate AI's promising results, achieving accuracies comparable to radiologists.
  • AI shows potential in improving image quality, segmenting lesions, and differentiating cancer significance.
  • AI models are being developed for classification into PI-RADS categories and Gleason scores.

Conclusions:

  • AI holds significant potential to enhance radiologist performance and prostate cancer management.
  • Prospective, multicenter studies are crucial to validate AI's full impact and clinical utility.
  • Emerging quality metrics are important for evaluating AI research in medical imaging.