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Abstract #3359

Deep learning for DSI parameter map generation without image pre-processing

Eric Kenneth Gibbons1, Kyler K. Hodgson2, Ganesh Adluru1, and Edward VR DiBella1

1Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, United States, 2Bioengineering, University of Utah, Salt Lake City, UT, United States

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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