AcceleraTed deep-LeArning for model-free and multi-Shell (ATLAS) DWI

In this work, we aim to accelerate diffusion weighted MRI (dMRI) by predicting diffusion -weighted images (DWIs) across different shells using deep learning (DL), while remaining independent of a diffusion-model constraint. The proposed approach enables the predictions of unacquired DWIs in multiple shells from a small set of acquired DWIs from a given shell. This relaxes the need for applying multiple diffusion gradient weightings for obtaining a fully-acquired dataset over multiple shells. Without the constraint of a diffusion model, accurate diffusion metrics over multiple diffusion models can potentially be obtained by acquiring a small number of DWIs.