Recent advances in diffusion spectrum imaging (DSI) have reduced scan time considerably. Through the use of deep learning, DSI parameter maps (NODDI, GFA, etc.) can be generated with only a fraction of the number of q-space samples compared to conventional acquisition strategies. However, image pre-processing prior to the deep learning parameter map generation step is a computational bottleneck. This abstract explores if this bottleneck can be bypassed entirely and use images straight from the scanner as CNN inputs. We show that the image pre-processing is not necessary to generate NODDI and GFA parameter maps--thereby avoiding the image processing computation time.
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