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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...

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Topographical Estimation of Visual Population Receptive Fields by fMRI
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Published on: February 3, 2015

Field map reconstruction in magnetic resonance imaging using Bayesian estimation.

Fabio Baselice1, Giampaolo Ferraioli, Aymen Shabou

  • 1Dipartimento per le Tecnologie, Università degli Studi di Napoli Parthenope, Naples, Italy. fabio.baselice@uniparthenope.it

Sensors (Basel, Switzerland)
|February 9, 2012
PubMed
Summary
This summary is machine-generated.

Accurate magnetic resonance imaging (MRI) field maps correct image distortions. This study presents a Bayesian and Graph Cuts method to reconstruct field maps from multiple MRI scans, improving image quality.

Keywords:
Magnetic Resonance ImagingMarkov Random Fieldbayesian estimationfield map estimationgraph-cutsphase unwrapping

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Computational Imaging

Background:

  • Magnetic Resonance Imaging (MRI) is susceptible to field inhomogeneities.
  • These inhomogeneities cause image artifacts like blur and distortion.
  • Accurate field maps are crucial for correcting these artifacts.

Purpose of the Study:

  • To develop a novel technique for reconstructing accurate magnetic resonance imaging (MRI) field maps.
  • To correct for field inhomogeneities in MRI data.
  • To improve the quality of MRI images.

Main Methods:

  • Utilized phase information from multiple complex MRI datasets.
  • Implemented a statistical estimation approach.
  • Employed a Bayesian estimator combined with the Graph Cuts optimization method.

Main Results:

  • Successfully reconstructed field maps using the proposed technique.
  • Demonstrated effectiveness on both simulated and real MRI data.
  • Validated the method's ability to correct for field inhomogeneities.

Conclusions:

  • The presented Bayesian and Graph Cuts-based method effectively reconstructs MRI field maps.
  • This technique offers a robust solution for correcting field inhomogeneities.
  • The approach has potential for improving diagnostic accuracy in MRI.