title: How does distortion correction correlate with anisotropic indices? A diffusion tensor imaging study.
abstract: OBJECTIVE
  The purpose of this study was to determine a suitable registration algorithm for diffusion tensor imaging (DTI) using conventional preprocessing tools [statistical parametric mapping (SPM) and automated image registration (AIR)] and to investigate how anisotropic indices for clinical assessments are affected by these distortion corrections.
  
  
  METHODS
  Brain DTI data from 15 normal healthy volunteers were used to evaluate four spatial registration schemes within subjects to correct image distortions: noncorrection, SPM-based affine registration, AIR-based affine registration and AIR-based nonlinear polynomial warping. The performance of each distortion correction was assessed using: (a) quantitative parameters: tensor-fitting error (Ef), mean dispersion index (MDI), mean fractional anisotropy (MFA) and mean variance (MV) within 11 regions of interest (ROI) defined from homogeneous fiber bundles; and (b) fiber tractography through the uncinate fasciculus and the corpus callosum. Fractional anisotropy (FA) and mean diffusivity (MD) were calculated to demonstrate the effects of distortion correction. Repeated-measures analysis of variance was used to investigate differences among the four registration paradigms.
  
  
  RESULTS
  AIR-based nonlinear registration showed the best performance for reducing image distortions with respect to smaller Ef (P<.02), MDI (P<.01) and MV (P<.01) with larger MFA (P<.01). FA was decreased to correct distortions (P<.0001) whether the applied registration was linear or nonlinear and was lowest after nonlinear correction (P<.001). No significant differences were found in MD.
  
  
  CONCLUSIONS
  In conventional DTI processing, anisotropic indices of FA can be misestimated by noncorrection or inappropriate distortion correction, which leads to an erroneous increase in FA. AIR-based nonlinear distortion correction would be required for a more accurate measurement of this diffusion parameter.
authors.affiliation: Department of Biomedical Engineering, Hanyang University, and Department of Radiology, Kangbuk Samsung Hospital, Seoul 133-605, South Korea.
authors.forename: Dae-Jin
authors.initials: DJ
authors.lastname: Kim
authors.forename: Hae-Jeong
authors.initials: HJ
authors.lastname: Park
authors.forename: Kyung-Whun
authors.initials: KW
authors.lastname: Kang
authors.forename: Yong-Wook
authors.initials: YW
authors.lastname: Shin
authors.forename: Jae-Jin
authors.initials: JJ
authors.lastname: Kim
authors.forename: Won-Jin
authors.initials: WJ
authors.lastname: Moon
authors.forename: Eun-Chul
authors.initials: EC
authors.lastname: Chung
authors.forename: In Young
authors.initials: IY
authors.lastname: Kim
authors.forename: Jun Soo
authors.initials: JS
authors.lastname: Kwon
authors.forename: Sun I
authors.initials: SI
authors.lastname: Kim
mesh_terms: D000328:Adult; D000465:Algorithms; D016880:Anisotropy; D001921:Brain / Q000378:metabolism*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted; D017711:Nonlinear Dynamics