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Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

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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

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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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Updated: Jan 18, 2026

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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Research-based clinical deployment of artificial intelligence algorithm for prostate MRI.

Stephanie A Harmon1, Jesse Tetreault2, Omer Tarik Esengur3

  • 1National Institutes of Health, Bethesda, USA. stephanie.harmon@nih.gov.

Abdominal Radiology (New York)
|May 26, 2025
PubMed
Summary
This summary is machine-generated.

Integrating artificial intelligence (AI) into Picture Archiving and Communications Systems (PACS) for prostate cancer imaging is feasible. This AI pipeline enables point-of-care utilization, improving diagnostic accuracy and biopsy targeting.

Keywords:
Artificial intelligenceDeep learningDeploymentMRIPACSProstate cancer

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Clinical integration of Artificial Intelligence (AI) algorithms into Picture Archiving and Communications Systems (PACS) remains a significant challenge.
  • AI-powered segmentation tools are crucial for improving the efficiency and accuracy of radiological interpretations.

Purpose of the Study:

  • To integrate an AI-based pipeline for prostate organ and intraprostatic lesion segmentation within a clinical PACS environment.
  • To enable point-of-care utilization of AI tools for prostate cancer diagnosis and biopsy planning.

Main Methods:

  • A previously trained AI model for prostate MRI segmentation was containerized using MONAI Deploy Express.
  • An inference server and PACS workflow were established for real-time AI algorithm evaluation.
  • Prospective evaluation in two phases: diagnostic imaging cohort and biopsy-based cohort.

Main Results:

  • Successful PACS deployment in 57/58 cases (phase one) and 40/40 cases (phase two) within one minute.
  • Stable AI model performance compared to independent validation studies.
  • Improved cancer detection rates when AI was used in conjunction with radiologist interpretation.

Conclusions:

  • Integration of multi-parametric AI algorithms into clinical PACS is feasible.
  • AI-generated outputs can be effectively utilized for downstream clinical tasks like biopsy targeting.