A comparative study of diffusion tensor field transformations

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.