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We evaluate a new approach for achieving diffusion MRI data with high spatialresolution, large volume coverage, and fast acquisition speed.  A recent method called gSlider-SMS enables whole-brain sub-millimeterdiffusion MRI with high signal-to-noise ratio (SNR) efficiency. However,despite the efficient acquisition, the resulting images can still suffer fromlow SNR due to the small size of the imaging voxels. This work proposes tomitigate the SNR problem by combining gSlider-SMS with a regularizedSNR-enhancing reconstruction approach.  Illustrative results show that, from gSlider-SMS data acquired over a span ofonly 25 minutes on a 3T scanner, the proposed method is able to produce 71 MRIimages (64 diffusion encoding orientations with $b=$1500 s/mm$^2$, and 7 imageswithout diffusion weighting) of the entire \emph{in vivo} human brain withnominal 0.66 mm spatial resolution. Using data acquired from 75 minutes ofacquisition as a gold standard reference, we demonstrate that the proposedSNR-ehancement procedure leads to substantial improvements in estimateddiffusion parameters compared to conventional gSlider reconstruction. Resultsalso demonstrate that the proposed method has advantages relative to denoisingmethods based on low-rank matrix modeling. A theoretical analysis of thetrade-off between spatial resolution and SNR suggests that the proposedapproach has high efficiency.  The combination of gSlider-SMS with advanced regularized reconstructionenables high-resolution quantitative diffusion MRI from a relatively fastacquisition.

Fast Sub-millimeter Diffusion MRI using gSlider-SMS and SNR-Enhancing Joint Reconstruction