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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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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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Updated: Jul 30, 2025

Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders
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Determining and Validating Population Differences in Magnetic Resonance Angiography Using Sparse Representation.

Steve Mendoza1, Fabien Scalzo2, Aichi Chien1

  • 1Department of Radiological Science, David Geffen School of Medicine at UCLA.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
|May 14, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning reveals gender-based variations in the circle of Willis (CoW) vasculature. This automated approach aids diagnostic radiology and complex algorithm development.

Keywords:
Brain VasculatureCerebral AngiographyCircle of WillisGender DifferenceMachine LearningMagnetic Resonance ImagingMedical ImagingMorphometry

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Area of Science:

  • Medical imaging
  • Radiology
  • Neuroscience

Background:

  • Population variations in vasculature are crucial for diagnostic radiology.
  • A robust preprocessing framework and data representation are essential for identifying these differences.

Purpose of the Study:

  • To develop a machine learning model for visualizing gender differences in the circle of Willis (CoW).
  • To establish a method for automatically detecting population variations in brain vasculature.

Main Methods:

  • Utilized a dataset of 570 individuals, with 389 processed for final analysis.
  • Employed machine learning techniques, including Support Vector Machines (SVM), to analyze and visualize CoW data.
  • Focused on identifying differences in specific image planes of the vasculature.

Main Results:

  • Identified statistically significant gender-based differences in one image plane of the circle of Willis.
  • Visualized these differences, confirming variations between the right and left sides of the brain using SVM.

Conclusions:

  • The developed process can automatically detect population variations in vasculature.
  • This methodology can assist in debugging and inferring complex machine learning models like SVM and deep learning.