How does distortion correction correlate with anisotropic indices? A diffusion tensor imaging study

  • Kim D
  • Park H
  • Kang K
 et al. 
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Purpose: 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. Materials and 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

Author-supplied keywords

  • Diffusion tensor imaging
  • Distortion correction
  • Evaluation
  • Image registration

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  • Dae Jin Kim

  • Hae Jeong Park

  • Kyung Whun Kang

  • Yong Wook Shin

  • Jae Jin Kim

  • Won Jin Moon

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