Diffusion weighted (DW) magnetic resonance images are often recorded with single shot multislice
imaging sequences, because of their short scanning times and robustness to motion. To minimize
noise and acquisition time, images are generally acquired with either anisotropic or isotropic
low resolution voxels, which impedes subsequent posterior image processing and visualization. In
this paper, we propose a super-resolution method for diffusion weighted imaging that combines
anisotropic multislice images to enhance the spatial resolution of diffusion tensor (DT) data. Each
DW image is reconstructed from a set of arbitrarily oriented images with a low through-plane
resolution. The quality of the reconstructed DW images was evaluated by DT metrics and tractography.
Experiments with simulated data, a hardware DTI phantom, as well as in vivo human
brain data were conducted. Our results show a significant increase in spatial resolution of the DT
data while preserving high signal to noise ratio.