Recovering high quality FODs from reduced number of diffusion weighted images using a model-driven deep learning architecture

SDNet utilises the learning ability of deep neural networks with the robustness of model-based approaches to produce high quality fibre orientation distributions (FODs) from a reduced set of multi-shell diffusion weighted images (DWI). The cascaded architecture, with data consistency layers throughout, makes use of model based prior knowledge and spatial correlations within the DWI signal to achieve state-of-the-art performance in both sum of squared errors and angular correlation coefficient. Our model also shows competitive results with respect to apparent fibre density error and peak amplitude error over a range of regions of interest.