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Removing background phase variations in susceptibility-weighted imaging using a fast, forward-field calculation.

Jaladhar Neelavalli1, Yu-Chung N Cheng, Jing Jiang

  • 1Department of Biomedical Engineering, Wayne State University, Detroit, Michigan, USA. jaladhar@wayne.edu

Journal of Magnetic Resonance Imaging : JMRI
|March 24, 2009
PubMed
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This study introduces a Fourier transform method to correct magnetic field variations in susceptibility-weighted imaging (SWI). The technique improves image quality, especially in brain regions near air-tissue interfaces, by reducing signal dropout.

Area of Science:

  • Medical Imaging
  • Biophysics

Background:

  • Susceptibility-weighted imaging (SWI) is sensitive to magnetic field variations.
  • Air-tissue interfaces in the head can cause significant magnetic field distortions in SWI data.
  • These distortions lead to signal dropout and artifacts, obscuring anatomical details.

Purpose of the Study:

  • To develop and validate a method for estimating and removing magnetic field variations caused by air-tissue interfaces in SWI.
  • To improve the quality and interpretability of SWI data by correcting for geometric artifacts.

Main Methods:

  • A Fourier transform-based method was employed to estimate field deviations from air-tissue interfaces (sinuses, mastoid).
  • The difference in magnetic susceptibility (Deltachi) between brain tissue and air was calculated.

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  • SWI data were corrected by subtracting the estimated phase from the original phase images.

    • Main Results:

    • The method significantly improved SWI data quality, particularly in frontal and midbrain regions.
    • Phase correction reduced signal dropout near air-tissue interfaces.
    • Previously obscured vessels and structures became visible after correction.

    Conclusions:

    • The Fourier transform-based field estimation is an effective 3D background phase removal technique for SWI.
    • This method enhances SWI image clarity, especially for forebrain and midbrain structures.
    • The technique offers a powerful solution for mitigating artifacts in susceptibility-weighted imaging.