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Modified iterative model based on data extrapolation method to reduce Gibbs ringing.

S Amartur1, E M Haacke

  • 1Department of Radiology, University Hospitals, Cleveland, OH.

Journal of Magnetic Resonance Imaging : JMRI
|May 1, 1991
PubMed
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This study introduces an iterative algorithm to reduce Gibbs artifacts in magnetic resonance imaging (MRI) by filtering and replacing data. The method effectively minimizes ringing artifacts in homogeneous regions but may thicken structures not fitting the model.

Area of Science:

  • Medical Imaging
  • Image Processing
  • Computational Science

Background:

  • Gibbs artifacts, or ringing artifacts, are common in magnetic resonance imaging (MRI) due to truncated k-space data.
  • These artifacts can obscure image details and affect diagnostic accuracy.

Purpose of the Study:

  • To investigate an iterative algorithm for reducing Gibbs artifacts in MRI.
  • To assess the algorithm's performance in terms of convergence and signal-to-noise ratio.

Main Methods:

  • An iterative algorithm combining edge-preserving filtering and data replacement was employed.
  • Filtering was performed in the complex image domain to address phase and magnitude discontinuities.
  • The algorithm models image features as delta functions or boxes to reconstruct missing high-frequency information.

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Main Results:

  • The algorithm demonstrated effectiveness in reducing ringing artifacts in homogeneous, isointense regions of MRI scans.
  • Simulated and clinical examples showed reasonable performance in artifact reduction.
  • The method may introduce spurious thickening in structures that deviate from the assumed step-edge models.

Conclusions:

  • The iterative filtering and data replacement algorithm is a viable method for mitigating Gibbs artifacts in MRI.
  • Careful consideration of structural models is necessary to avoid introducing new image distortions.