Submillimeter isotropic diffusion imaging (DI) is made difficult by long acquisition time and low SNR. Slider-SMS acquisition¹ has recently been proposed to provide large improvement to SNR efficiency for DI, by allowing a large number of imaging slices to be acquire simultaneously through the combined use of SMS parallel imaging and super-resolution approaches. Further improvement to this acquisition with a new generalized Slider approach (gSlider) is also being proposed², which utilizes RF encoding to improve the orthogonality of the slice-encoding basis for super-resolution, to enable high quality 5 simultaneous slice super-resolution, which when combines with SMS parallel imaging can achieve highly efficient 10 simultaneous slices DI (gSlider x MB = 5x2). In this work, we provide further improvement to this acquisition by incorporating the framework of Compressed Sensing for Super-Resolution Diffusion (CS-RSD) proposed in³ to gSlider-SMS. This enables acquisition acceleration through under-sampling of the gSlider slice-encoded super-resolution acquisitions needed for super-resolution reconstruction of each diffusion direction (with different random undersampling for different diffusion directions). Such acquisition undersampling is made possible through sparsity enforce reconstruction where the diffusion signal in each voxel of the final high-resolution image is enforced to be sparse in the basis of spherical ridgelets. We demonstrate that the incorporation of the CS-RSD approach enables a further 2x efficiency gain to an already highly efficient 10 simultaneous slice gSlider-SMS acquisition. This permits, for the first time, high quality 860μm whole-brain DI at high b-value to be perform in a relevant clinical timeframe of 8 minutes. gSlider-SMS data with 10 simultaneous slice acquisition (gSlider×MB = 5×2) were acquired in a healthy volunteer on the 3T CONNECTOM system using custom-built 64-channel array. Imaging parameters were: 860μm iso; FOV 220×163.4×130 mm³; p.f. 6/8; TE = 64ms and TR per thick-slice volume = 4.2s; effective echo spacing = 0.32ms, 64 directions at b=2000 s/mm² with interspersed b0 every 10 volumes, where for each diffusion direction, imaging were performed 5 times for different RF gSlider encoding, total scan-time ~25 min. Background phase removal was performed using real value diffusion algorithm⁴. Reconstruction for full sampled Slider encoding data were performed using Tikhonov regularized super-resolution reconstruction to represent fully-sampled acquisition for comparison. For incorporation of CS-RSD, the 5 different gSlider slice-encode imaging per diffusion direction were randomly undersampled to provide 2x acceleration to result in an effective acquisition time of ~12 min. This accelerated acquisition is reconstructed using algorithm proposed in3 and compared with the fully sampled reconstruction. Figure (1) shows the acquired thick-slice images and the reconstructed high-resolution diffusion-weighted images obtained using the Tikhonov regularization and the compressed-sensing approach, respectively. The high-resolution image obtained using the compressed-sensing approach has the lowest noise level due to the sparse representation using spherical ridgelets. Figure (2) shows the glyph results in a cortical gray-matter region of the three diffusion MRI volumes, respectively, with color-coded by the principle direction of the underlying diffusion tensors. The glyphs obtained using the compressed-sensing method have a similar pattern as in the result given by the Tikhonov regularization.