gNET: gSlider Self-Supervised Neural Network for Accelerated Reconstruction of Super-resolution Diffusion MRI

Motivation: gSlider utilizes radio-frequency encoding to acquire high and isotropic resolution brain diffusion-MRI with high SNR. However, this comes at the cost of prolonged acquisition time, which also increases the sensitivity to motion. Goal(s): This work proposes gSlider Network (gNET) to accelerate gSlider from acquisitions with jointly subsampled RF- and q-space. Approach: The self-supervised model was trained and tested on a 1mm3 resolution BUDA-gSlider dataset (Tacq = 32 min). FSL and the DIMOND self-supervised were used to estimate the diffusion parameters. Results: gNET achieved an acceleration factor of R=2 and, when combined with DIMOND, reached a total R=4-fold (Tacq = 8 min). Impact: gNET facilitates super-resolution dMRI by reducing the acquisition time by 4-fold with high fidelity. Its application may propel new discoveries in the neuroscientific field and the clinical translation of the gSlider framework.