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MRI artifact correction using sparse + low-rank decomposition of annihilating filter-based hankel matrix.

Kyong Hwan Jin1,2, Ji-Yong Um1, Dongwook Lee1

  • 1Department of Bio and Brain Engineering, Korea Advanced Institute of Science & Technology (KAIST), 373-1 Guseong-Dong Yuseong-Gu, Daejon, 305-701, Republic of Korea.

Magnetic Resonance in Medicine
|July 29, 2016
PubMed
Summary

This study introduces a new compressed sensing method to remove various magnetic resonance imaging (MRI) artifacts. The technique effectively corrects common MRI artifacts like herringbone, motion, and zipper, improving image quality without distortion.

Keywords:
ADMMLMaFitMRI artifactannihilating filtercrisscross artifactherringbone artifactk-space weightingmotion artifactstructured low rank Hankel matrix completionzipper artifact

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Biomedical Engineering

Background:

  • Magnetic Resonance Imaging (MRI) artifacts arise from system instability, patient motion, and field inhomogeneities.
  • Current methods for MRI artifact correction often require additional scans, increasing time and cost.
  • Artifacts are typically considered irreversible, necessitating complex workarounds.

Purpose of the Study:

  • To propose a novel compressed sensing-based approach for the removal of various MRI artifacts.
  • To develop a method that overcomes the limitations of existing artifact correction techniques.
  • To provide a robust solution for improving the quality of MRI scans.

Main Methods:

  • Utilized a sparse + low-rank decomposition framework employing a Hankel matrix.
  • Leveraged the annihilating filter based low-rank Hankel matrix approach.
  • Employed the alternating direction method of multipliers algorithm for cost function minimization.

Main Results:

  • The proposed algorithm successfully corrected common MRI artifacts, including herringbone (crisscross), motion, and zipper artifacts.
  • Experimental results demonstrated artifact correction without introducing image distortion.
  • The method proved effective in identifying and removing sparse outliers representing artifacts.

Conclusions:

  • The developed method offers a robust solution for correcting diverse MRI artifacts.
  • The approach is particularly effective for artifacts representable as sparse outliers in k-space.
  • This technique enhances the reliability and diagnostic value of MRI scans.