@article{oai:repo.qst.go.jp:00045914,
 author = {Abe, Osamu and Takao, Hidemasa and Gonoi, Wataru and Sasaki, Hiroki and Murakami, Mizuho and Kabasawa, Hiroyuki and Kawaguchi, Hiroshi and Goto, Masami and Yamada, Haruyasu and Yamasue, Hidenori and Kasai, Kiyoto and Aoki, Shigeki and Ohtomo, Kuni and 川口 拓之},
 issue = {8},
 journal = {Neuroradiology},
 month = {May},
 note = {Introduction  
Diffusion tensor imaging (DTI) has provided important insights into the neurobiological basis for normal development and aging and various disease processes in the central nervous system. The aim of this article is to review the current protocols for DTI acquisition and preprocessing and statistical testing for a voxelwise analysis of DTI, focused on statistical parametric mapping (SPM) and tract-based spatial statistics (TBSS). 
Methods  
We tested the effects of distortion correction induced by gradient nonlinearity on fractional anisotropy (FA) maps or FA skeletons processed via two SPM-based methods (coregistration and FA template methods), or TBSS-based method, respectively. 
Results  
With two SPM-based methods, we found similar results in some points (e.g., significant FA elevation for uncorrected images in anterior-dominant white matter and for corrected images in bilateral middle cerebellar peduncles) and different results in other points (e.g., significantly larger FA for corrected images with coregistration method, but significantly smaller with FA template method in bilateral internal capsules, extending to corona radiata, and semioval centers). In contrast, there was no area with significant difference between uncorrected and corrected FA skeletons with TBSS-based method. 
Conclusion  
The discrepancy among these results was not explained in full, but possible explanations were misregistration and smoothing for the SPM-based methods and insensitivity to FA changes outside the local centers of white matter bundles for TBSS-based method.},
 pages = {699--710},
 title = {Voxel-based analysis of the diffusion tensor},
 volume = {52},
 year = {2010}
}