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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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Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
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Updated: Jul 25, 2025

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Radiomics based automated quality assessment for T2W prostate MR images.

Linda C P Thijssen1, Maarten de Rooij1, Jelle O Barentsz1

  • 1Radboud University Medical Center, Nijmegen, the Netherlands.

European Journal of Radiology
|June 24, 2023
PubMed
Summary

Radiomics AI can automatically identify poor quality T2-weighted (T2W) MRI scans for prostate cancer detection. This automated assessment improves image quality, reduces repeat scans, and enhances diagnostic accuracy in clinical practice.

Keywords:
Artificial IntelligenceHealth careMultiparametric magnetic resonance imagingProof of concept studyProstatic neoplasmsQuality assurance

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in radiology
  • Prostate cancer diagnostics

Background:

  • Magnetic resonance imaging (MRI) is crucial for prostate cancer detection, but image quality variability impacts reliability.
  • Objective and automated assessment of prostate MRI quality is needed to ensure adequate acquisitions and improve diagnostic accuracy.

Purpose of the Study:

  • To investigate the feasibility of using Blind/referenceless image spatial quality evaluator (Brisque) and radiomics for automated quality assessment of T2-weighted (T2W) prostate MRI.
  • To develop and validate a model for distinguishing clinically acceptable from unacceptable T2W prostate MRI quality.

Main Methods:

  • T2W prostate MRI images from 140 patients were assessed for quality by two readers using a five-point Likert scale.
  • Radiomics and Brisque features were extracted from the prostate region to train a Linear Discriminant Analysis (LDA) model.
  • A 5-fold cross-validation repeated 200 times was used to evaluate model performance, including classification accuracy and ROC-AUC.

Main Results:

  • The automated model achieved an accuracy of 85.4% ± 5.5% on an independent test set.
  • The receiver operating curve - area under the curve (ROC-AUC) was 0.856, indicating good discrimination performance.
  • Out of 140 images, 34 were classified as clinically unacceptable and 106 as clinically acceptable.

Conclusions:

  • Radiomics-based AI can effectively automate the detection of suboptimal T2W prostate MRI quality.
  • Automated quality assessment can lead to improved image acquisition, reduced scan retakes, and enhanced diagnostic reliability in prostate cancer imaging.