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SNORE: spike noise removal and detection.

T F Foo1, N S Grigsby, J D Mitchell

  • 1Appl. Sci. Lab., GE Med. Syst., Milwaukee, WI.

IEEE Transactions on Medical Imaging
|January 1, 1994
PubMed
Summary

This study introduces a novel method to detect and remove random spike noise in magnetic resonance (MR) k-space data. The technique effectively reduces artifacts in MR images caused by this noise.

Area of Science:

  • Medical Imaging
  • Signal Processing
  • Biophysics

Background:

  • Random spike noise in k-space data is a significant issue in Magnetic Resonance (MR) imaging.
  • This noise can lead to severe artifacts, such as corduroy-type patterns and elevated noise levels, degrading image quality.
  • Existing methods may not adequately address the spatial variability of noise in MR raw data.

Purpose of the Study:

  • To develop and describe an effective method for detecting and removing random spike noise from MR raw data (k-space data).
  • To reduce or eliminate image artifacts caused by random spike noise in MR images.
  • To improve the overall quality and diagnostic utility of MR images.

Main Methods:

  • The proposed method involves applying a spatially varying threshold to the k-space data.

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  • Data points exceeding the location-specific threshold are identified as noise.
  • Identified noisy data points are replaced using a local complex average of neighboring data points or an alternative suitable data replacement scheme.

    • Main Results:

    • The method successfully detects and removes random spike noise from MR k-space data.
    • Significant reduction or elimination of corduroy-type artifacts and other noise-induced artifacts in MR images was achieved.
    • The application of a spatially varying threshold proved effective in handling non-uniform noise distributions.

    Conclusions:

    • The described method provides an efficient approach to mitigate random spike noise in MR imaging.
    • This technique enhances MR image quality by reducing artifacts, leading to more reliable diagnostic information.
    • The spatially varying threshold approach offers a robust solution for noise removal in MR raw data.