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Related Concept Videos

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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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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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
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Synthetic quantitative MRI through relaxometry modelling.

Martina F Callaghan1, Siawoosh Mohammadi1,2, Nikolaus Weiskopf1,3

  • 1Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK.

NMR in Biomedicine
|October 19, 2016
PubMed
Summary
This summary is machine-generated.

Synthetic quantitative MRI (qMRI) creates accurate tissue microstructure maps from existing data. This novel approach improves motion-corrupted data analysis and reduces scan time by synthesizing magnetization transfer (MT) maps.

Keywords:
magnetization transferrelaxometrysynthetic quantitative MRI

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Area of Science:

  • Biomedical Imaging
  • Quantitative Magnetic Resonance Imaging (qMRI)

Background:

  • Quantitative MRI (qMRI) offers standardized, biologically relevant metrics for in vivo histology.
  • Interdependencies between qMRI parameters are increasingly understood through advanced models.
  • Image synthesis combined with biophysical models offers a novel approach to generating synthetic qMRI data.

Purpose of the Study:

  • To investigate the utility of synthetic qMRI within a linear relaxometry model.
  • To demonstrate artifact correction in quantitative MRI using synthetic data.
  • To synthesize Magnetization Transfer (MT) saturation maps without direct MT-weighted acquisition, reducing specific absorption rate (SAR).

Main Methods:

  • Exploited the over-determined nature of a linear relaxometry model to synthesize artifact-free quantitative maps from motion-corrupted data.
  • Developed a method to synthesize MT saturation maps using existing qMRI data, bypassing the need for dedicated MT-weighted scans.
  • Applied synthetic MT maps to segment deep grey matter structures.

Main Results:

  • Successfully synthesized artifact-free quantitative MRI maps from motion-corrupted datasets.
  • Generated synthetic MT saturation maps, reducing acquisition time and specific absorption rate (SAR), particularly beneficial for ultra-high field MRI.
  • Demonstrated improved segmentation of deep grey matter structures using synthetic MT maps compared to T1-weighted images or R1 maps.

Conclusions:

  • Synthetic qMRI, utilizing biophysical models, enhances information extraction from qMRI protocols.
  • The proposed method offers a promising avenue for improving data quality and reducing scan burden in quantitative MRI.
  • Synthetic qMRI facilitates a deeper understanding of the interrelationships between various qMRI parameters and their relation to tissue microstructure.