title: Smoothing that does not blur: effects of the anisotropic approach for evaluating diffusion tensor imaging data in the clinic.
abstract: OBJECTIVE
  To compare the effects of anisotropic and Gaussian smoothing on the outcomes of diffusion tensor imaging (DTI) voxel-based (VB) analyses in the clinic, in terms of signal-to-noise ratio (SNR) enhancement and directional information and boundary structures preservation.
  
  
  METHODS
  DTI data of 30 Alzheimer's disease (AD) patients and 30 matched control subjects were obtained at 3T. Fractional anisotropy (FA) maps with variable degrees and quality (Gaussian and anisotropic) of smoothing were created and compared with an unsmoothed dataset. The two smoothing approaches were evaluated in terms of SNR improvements, capability to separate differential effects between patients and controls by a standard VB analysis, and level of artifacts introduced by the preprocessing.
  
  
  RESULTS
  Gaussian smoothing regionally biased the FA values and introduced a high variability of results in clinical analysis, greatly dependent on the kernel size. On the contrary, anisotropic smoothing proved itself capable of enhancing the SNR of images and maintaining boundary structures, with only moderate dependence of results on smoothing parameters.
  
  
  CONCLUSIONS
  Our study suggests that anisotropic smoothing is more suitable in DTI studies; however, regardless of technique, a moderate level of smoothing seems to be preferable considering the artifacts introduced by this manipulation.
authors.affiliation: MARBILab, Museo storico della fisica e Centro di studi e ricerche Enrico Fermi, c/o Fondazione Santa Lucia, Roma, Italy.
authors.forename: Marta
authors.initials: M
authors.lastname: Moraschi
authors.forename: Gisela E
authors.initials: GE
authors.lastname: Hagberg
authors.forename: Margherita
authors.initials: M
authors.lastname: Di Paola
authors.forename: Gianfranco
authors.initials: G
authors.lastname: Spalletta
authors.forename: Bruno
authors.initials: B
authors.lastname: Maraviglia
authors.forename: Federico
authors.initials: F
authors.lastname: Giove
mesh_terms: D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology*; D016880:Anisotropy; D016477:Artifacts; D001921:Brain / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity