Schizophrenia is a serious and disabling mental disorder. Diffusion tensor imaging (DTI) studies performed on
schizophrenia have demonstrated white matter degeneration either due to loss of myelination or deterioration of fiber
tracts although the areas where the changes occur are variable across studies. Most of the population based studies
analyze the changes in schizophrenia using scalar indices computed from the diffusion tensor such as fractional
anisotropy (FA) and relative anisotropy (RA). The scalar measures may not capture the complete information from the
diffusion tensor. In this paper we have applied the RADTI method on a group of 9 controls and 9 patients with
schizophrenia. The RADTI method converts the tensors to log-Euclidean space where a linear regression model is
applied and hypothesis testing is performed between the control and patient groups. Results show that there is a
significant difference in the anisotropy between patients and controls especially in the parts of forceps minor, superior
corona radiata, anterior limb of internal capsule and genu of corpus callosum. To check if the tensor analysis gives a
better idea of the changes in anisotropy, we compared the results with voxelwise FA analysis as well as voxelwise
geodesic anisotropy (GA) analysis.
Diffusion imaging provides the ability to study white matter connectivity and integrity noninvasively. Diffusion
weighted imaging contains orientation information that must be appropriately reoriented when applying spatial
transforms to the resulting imaging data. Alexander et al. have introduced two methods to resolve the reorientation
problem. In the first method, the rotation matrix is computed from the transform and the tensors are reoriented. The
second method called as the preservation of principal direction (PPD) method, takes into account the deformation and
rotation components to estimate the rotation matrix. These methods cannot be directly used for higher order diffusion
models (e.g. Q-ball). We have introduced a novel technique called gradient rotation where the rotation is directly applied
to the diffusion sensitizing gradients providing a voxel by voxel estimate of the diffusion gradients instead of a volume
of by volume estimate. A PPD equivalent gradient rotation can be computed using principal component analysis (PCA).
Four subjects were spatially normalized to a template subject using a multistage registration sequence that includes
nonlinear diffeomorphic demons registration. Comparative results of all four methods have been shown. It can be
observed that all the methods work closely to each other, PPD (original and gradient equivalent) being slightly better
than rigid rotation, based on the fact that it includes the shear and scale component. Results also demonstrate that the
multistage registration is a viable method for spatial normalization of diffusion models.
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