Playing with k-space lines: downsampling through deep learning to improve clinical sensitivity of diffusion imaging

Motivation: The long acquisition time of diffusion-weighted (DW) imaging hinders its adoption in the clinic for studying pathological microstructural changes in vivo. Goal(s): The goal of this study was to reduce these long acquisition times by performing downsampling in k-space while maintaining clinical sensitivity. Approach: Deep learning was used to transform k-space data to DW images. Experiments were performed eliminating 30% of k-space lines using different methods. Results: DW images obtained with k-space down-sampling showed a reduction in artefacts, while fractional anisotropy images fitted from the network output appeared to have increased clinical sensitivity. Impact: This work paves the way for the design of acquisition strategies for fast diffusion-imaging. Through deep learning, it was possible to downsample k-space data in several ways, while obtaining a reduction in artefacts, with a potential increase in clinical sensitivity.