  Diffusion MRI (dMRI) is a unique imaging technique for in vivo
characterization of tissue microstructure and white matter pathways. However,
its relatively long acquisition time implies greater motion artifacts when
imaging, for example, infants and Parkinson's disease patients. To accelerate
dMRI acquisition, we propose in this paper (i) a diffusion encoding scheme,
called Slice-Interleaved Diffusion Encoding (SIDE), that interleaves each
diffusion-weighted (DW) image volume with slices that are encoded with
different diffusion gradients, essentially allowing the slice-undersampling of
image volume associated with each diffusion gradient to significantly reduce
acquisition time, and (ii) a method based on deep learning for effective
reconstruction of DW images from the highly slice-undersampled data. Evaluation
based on the Human Connectome Project (HCP) dataset indicates that our method
can achieve a high acceleration factor of up to 6 with minimal information
loss. Evaluation using dMRI data acquired with SIDE acquisition demonstrates
that it is possible to accelerate the acquisition by as much as 50 folds when
combined with multi-band imaging.
