Diffusion Tensor Images Upsampling: a Registration-based Approach

Diffusion weighted images (DWI), from which the corresponding diffusion tensor images (DTI) are estimated, are commonly acquired with anisotropic discretizations. Traditional scene-based interpolation methods to upsample diffusion weighted images do not lead to satisfactory results since they do not exploit structural information from the images. In this paper, we present a DTI upsampling framework that incorporates the underlying anatomical shape information by means of non-rigid inter-slice registration. A strategy is proposed to reorient the interpolated tensor in order to maintain its proper orientation. We tested our framework on phantom as well as on real data sets. Both results show that our method is able to produce more accurate results, in terms of both precisions of DW/DTI interpolation and diffusion tensor orientation.