High-Resolution Diffusion-Weighted Imaging With Self-Gated Self-Supervised Unrolled Reconstruction.

PURPOSE: High-resolution diffusion-weighted imaging (DWI) is clinically demanding. The purpose of this work is to develop an efficient self-supervised algorithm unrolling technique for submillimeter-resolution DWI. METHODS: We developed submillimeter DWI acquisition utilizing multi-band multi-shot EPI with diffusion shift encoding. We unrolled the alternating direction method of multipliers (ADMM) to perform scan-specific self-gated self-supervised DeepDWI learning for multi-shot echo planar imaging with diffusion shift encoding on a clinical 7 T scanner. RESULTS: We demonstrate that (1) ADMM unrolling is generalizable across slices, (2) ADMM unrolling outperforms multiplexed sensitivity-encoding (MUSE) and compressed sensing with locally-low rank (LLR) regularization in terms of image sharpness, tissue continuity, and motion robustness, and (3) ADMM unrolling enables clinically feasible inference time. CONCLUSION: Our proposed ADMM unrolling enables whole brain DWI of 21 diffusion volumes at 0.7 mm isotropic resolution and 10 min scan, and shows higher signal-to-noise ratio (SNR), clearer tissue delineation, and improved motion robustness, which makes it plausible for clinical translation.