title: Susceptibility phase imaging with improved image contrast using moving window phase gradient fitting and minimal filtering.
abstract: OBJECTIVE
  To enhance image contrast in susceptibility phase imaging using a new method of background phase removal.
  
  
  METHODS
  A background phase removal method is proposed that uses the spatial gradient of the raw phase image to perform a moving window third-order local polynomial estimation and correction of the raw phase image followed by minimal high pass filtering. The method is demonstrated in simulation, 10 healthy volunteers, and 5 multiple sclerosis patients in comparison to a standard phase filtering approach.
  
  
  RESULTS
  Compared to standard phase filtering, the new method increased phase contrast with local background tissue in subcortical gray matter, cortical gray matter, and multiple sclerosis lesions by 67% ± 33%, 13% ± 7%, and 48% ± 19%, respectively (95% confidence interval). In addition, the new method removed more phase wraps in areas of rapidly changing background phase.
  
  
  CONCLUSIONS
  Local phase gradient fitting combined with minimal high pass filtering provides better tissue depiction and more accurate phase quantification than standard filtering.
authors.affiliation: Department of Biomedical Engineering, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Walsh
authors.forename: Amir
authors.initials: A
authors.lastname: Eissa
authors.forename: Gregg
authors.initials: G
authors.lastname: Blevins
authors.forename: Alan H
authors.initials: AH
authors.lastname: Wilman
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009103:Multiple Sclerosis / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted