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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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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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Applications Of NMR In Biology01:25

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Nuclear magnetic resonance (NMR) spectroscopy is a very valuable analytical technique for researchers. It has been used for more than 50 years as an analytical tool. F. Bloch and E. Purcell formulated NMR in 1946 and won the 1952 Nobel Prize in Physics  for their work. Biological macromolecules such as proteins, nucleic acids, lipids, and organic molecules including pharmaceutical compounds, can be studied using this versatile tool that exploits the magnetic properties of certain nuclei.
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Nuclear magnetic resonance (NMR) is a phenomenon exhibited by certain nuclei that can absorb characteristic radio frequency radiation under certain conditions. NMR has been extensively applied in molecular spectroscopy and medical diagnostic imaging. In both these applications, the molecule or subject under study is placed in a magnetic field and irradiated with radio frequency energy.
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When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
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Deducing magnetic resonance neuroimages based on knowledge from samples.

Yuwei Jiang1, Feng Liu2, Mingxia Fan3

  • 1Shanghai Key Laboratory of Magnetic Resonance, MOE & Shanghai Key Laboratory of Brain Functional Genomics, Institute of Cognitive Neuroscience, East China Normal University, Shanghai 200062, PR China; Department of Psychiatry, Columbia University & Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, 10032, USA.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|August 16, 2017
PubMed
Summary

This study introduces a knowledge-based method to repair magnetic resonance imaging (MRI) data with poor contrast by optimizing imaging parameters for individual participants. The technique improves MRI data quality and enables personalized imaging protocols.

Keywords:
Analogical reasoningContrast improvementKnowledge-based deducingPersonalized imagingRelaxation time

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Computational Biology

Background:

  • Individual anatomical and physiological variations necessitate tailored imaging parameters in Magnetic Resonance Imaging (MRI).
  • Standard MRI protocols may not yield optimal contrast for all participants due to inherent biological variance.

Purpose of the Study:

  • To develop a knowledge-based method for repairing MRI data with suboptimal contrast.
  • To enable the acquisition of MRI data as if individually optimized imaging parameters were used.
  • To facilitate personalized MRI protocols.

Main Methods:

  • Utilized analogical reasoning to infer voxel-wise relaxation properties based on morphological and biological similarity.
  • Implemented a framework involving intensity normalization, tissue segmentation, and relaxation time and image deduction.

Main Results:

  • Preliminary validation on 5 normal and 9 clinical 3T MRI datasets demonstrated effective contrast improvement.
  • Deduced imaging data using optimized parameters showed a high correlation with actual acquired data.
  • The method successfully improved contrast in real MRI data.

Conclusions:

  • The proposed method offers a novel approach to repair MRI data with suboptimal contrast using deduced relaxation times.
  • This technique facilitates the optimization of MRI protocols for individual participants, enabling personalized MR imaging.