title: Filtered deconvolution of a simulated and an in vivo phase model of the human brain.
abstract: OBJECTIVE
  To remove spatial patterns in gradient echo phase images which are caused by susceptibility differences between different tissue types using filtered deconvolution and to evaluate deconvolution effects.
  
  
  METHODS
  A realistic simulated susceptibility map of the human brain was built and used to evaluate the effects of filtered deconvolution. The simulated susceptibility map was convolved with a filter kernel representing a magnetic dipole resulting in a simulated phase map. The artificial phase map was superimposed with different noise levels and deconvolved using different deconvolution kernels. The resulting contrast-to-noise ratios between white and gray matter of the deconvolved data provide an estimate for an optimal deconvolution kernel for a given noise level. These results were used to deconvolve an in vivo phase model representing the average of 30 phase data sets and also individual phase data acquired at 7 Tesla.
  
  
  RESULTS
  The deconvolved phase model shows a better anatomical agreement with the corresponding magnitude than the original phase model (5% higher kappa coefficient). Visual inspection of the deconvolved individual phase shows a more consistent delineation of blood vessels.
  
  
  CONCLUSIONS
  Filtered deconvolution of SWI phase is possible when an appropriate filter kernel is used. This helps to improve region of interest definition as unrealistic phase patterns are removed.
authors.affiliation: Medical University of Vienna, Department of Radiology, Vienna, Austria.
authors.forename: Günther
authors.initials: G
authors.lastname: Grabner
authors.forename: Siegfried
authors.initials: S
authors.lastname: Trattnig
authors.forename: Markus
authors.initials: M
authors.lastname: Barth
mesh_terms: D000465:Algorithms; D001808:Blood Vessels / Q000473:pathology; D001921:Brain / Q000098:blood supply / Q000473:pathology*; D003198:Computer Simulation; D005583:Fourier Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D012984:Software