How does distortion correction correlate with anisotropic indices? A diffusion tensor imaging study.
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.
Kim
Dae-Jin
DJ
Department of Biomedical Engineering, Hanyang University, and Department of Radiology, Kangbuk Samsung Hospital, Seoul 133-605, South Korea.
Park
Hae-Jeong
HJ
Kang
Kyung-Whun
KW
Shin
Yong-Wook
YW
Kim
Jae-Jin
JJ
Moon
Won-Jin
WJ
Chung
Eun-Chul
EC
Kim
In Young
IY
Kwon
Jun Soo
JS
Kim
Sun I
SI
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