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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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...
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

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,...
Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Related Experiment Video

Updated: Jul 6, 2026

Making MR Imaging Child's Play - Pediatric Neuroimaging Protocol, Guidelines and Procedure
15:18

Making MR Imaging Child's Play - Pediatric Neuroimaging Protocol, Guidelines and Procedure

Published on: July 30, 2009

Deep learning for contrast-enhanced MRI in pediatric brain imaging.

Anna Macula1,2, Giovanni Morana3, Fiorenza Coppola3

  • 1Department of Physics, University of Turin, Turin, Italy. anna.macula@unito.it.

Neuroradiology
|July 4, 2026
PubMed
Summary

A deep learning algorithm for brain MRI contrast amplification, trained on adults, shows significant improvement in pediatric cases. This AI tool enhances lesion visualization and is preferred in most pediatric brain MRI scans.

Keywords:
BrainContrast amplificationDeep learningMRIPediatric

Related Experiment Videos

Last Updated: Jul 6, 2026

Making MR Imaging Child's Play - Pediatric Neuroimaging Protocol, Guidelines and Procedure
15:18

Making MR Imaging Child's Play - Pediatric Neuroimaging Protocol, Guidelines and Procedure

Published on: July 30, 2009

Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Radiology
  • Pediatric Neuroradiology

Background:

  • Deep learning algorithms are increasingly used for medical image analysis.
  • Contrast amplification in brain MRI can improve lesion detection.
  • Generalizability of AI models across different patient populations is crucial.

Purpose of the Study:

  • To evaluate the cross-population generalization of a deep learning algorithm for contrast amplification in pediatric brain MRI.
  • To assess the performance of an adult-trained algorithm on pediatric subjects, including infants (0-2 years).

Main Methods:

  • A retrospective study of 22 pediatric brain tumor cases (0-17 years).
  • Input: T1-weighted pre- and post-contrast MRI images.
  • Output: Contrast-amplified images processed with HDR algorithm.
  • Evaluation: Quantitative (CNR, CEP, LBR) and qualitative (Likert scale) assessments by neuroradiologists.
  • Anatomical similarity assessed using SSIM and log-Jacobian range.

Main Results:

  • Contrast-amplified images showed significant increases in CNR (+186.5%), LBR (+61.9%), and CEP (+110.4%).
  • Qualitative assessment revealed comparable lesion visualization, with majority preference for amplified images by neuroradiologists (54.5%–81.8%).
  • High anatomical similarity (average SSIM: 0.98) with no significant anatomical differences.

Conclusions:

  • The deep learning algorithm effectively enhances quantitative contrast metrics in pediatric brain MRI.
  • The algorithm demonstrates cross-population applicability, performing well on pediatric patients despite being trained on adult data.
  • Contrast amplification shows promise for improving diagnostic accuracy in pediatric brain MRI.