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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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Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring
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Three-dimensional motion corrected sensitivity encoding reconstruction for multi-shot multi-slice MRI: Application to

Lucilio Cordero-Grande1, Emer J Hughes1, Jana Hutter1

  • 1Centre for the Developing Brain and Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK.

Magnetic Resonance in Medicine
|June 20, 2017
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Summary

This study introduces a novel method for reconstructing magnetic resonance imaging scans, effectively correcting for rigid motion in multi-shot, multi-slice brain imaging to enhance image quality.

Keywords:
image reconstructionmagnetic resonancemotion correctionmulti-shot multi-slice imagesneonatal brain imaging

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Area of Science:

  • Medical Imaging
  • Neuroimaging
  • Magnetic Resonance Imaging

Background:

  • Motion artifacts significantly degrade the quality of multi-shot, multi-slice magnetic resonance imaging (MRI).
  • Accurate reconstruction of structural brain scans is crucial for reliable diagnosis and research.

Purpose of the Study:

  • To present a new methodology for reconstructing multi-shot, multi-slice MRI data.
  • To address both within-plane and through-plane rigid motion in anatomical brain imaging.
  • To demonstrate its application in improving structural brain MRI.

Main Methods:

  • An iterative approach alternating between motion estimation and image reconstruction.
  • Utilizing a shared objective function to refine 3D motion states for each shot and slice.
  • Employing overlapped slices and super-resolution for through-plane motion recovery and outlier rejection for artifact removal.

Main Results:

  • Significantly reduced artifacts in 1883 neonatal brain image volumes.
  • Demonstrated correction for motion and robustness against corrupted data.
  • Achieved further artifact suppression and resolution recovery by combining multi-view reconstructions.

Conclusions:

  • The proposed method effectively mitigates rigid motion in multi-shot, multi-slice anatomical brain scans.
  • A substantial improvement in image quality was observed across a large dataset.
  • This technique offers a robust solution for enhancing MRI data quality.