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Susceptibility phase imaging with improved image contrast using moving window phase gradient fitting and minimal

Andrew J Walsh1, Amir Eissa, Gregg Blevins

  • 1Department of Biomedical Engineering, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada.

Journal of Magnetic Resonance Imaging : JMRI
|August 7, 2012
PubMed
Summary

A novel background phase removal technique significantly improves image contrast in susceptibility phase imaging. This method enhances visualization of brain structures and lesions compared to standard filtering approaches.

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Area of Science:

  • Medical Imaging
  • Neuroimaging
  • Biophysics

Background:

  • Susceptibility phase imaging is crucial for visualizing brain structures.
  • Existing phase filtering methods can limit image contrast and quantification accuracy.
  • Background phase variations pose a challenge in accurate image interpretation.

Purpose of the Study:

  • To develop and evaluate a new method for background phase removal in susceptibility phase imaging.
  • To enhance image contrast and improve tissue depiction.
  • To increase the accuracy of phase quantification in neuroimaging.

Main Methods:

  • A novel background phase removal technique utilizing spatial phase gradients and local polynomial estimation.
  • Implementation of a moving window third-order local polynomial fitting and correction.
  • Application of minimal high-pass filtering post-correction.
  • Validation through simulations, healthy volunteers, and multiple sclerosis patients.

Main Results:

  • The new method demonstrated a significant increase in phase contrast compared to standard filtering.
  • Phase contrast improvements were observed in subcortical gray matter (67% ± 33%), cortical gray matter (13% ± 7%), and MS lesions (48% ± 19%).
  • Enhanced removal of phase wraps in regions with rapid background phase changes was noted.

Conclusions:

  • Local phase gradient fitting combined with minimal high-pass filtering offers superior tissue depiction.
  • The proposed method provides more accurate phase quantification than traditional filtering techniques.
  • This advancement has the potential to improve diagnostic capabilities in neuroimaging.