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Abstract #4308

50-Fold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE)

Yoonmi Hong1, Wei-Tang Chang1, Geng Chen2, Ye Wu1, Weili Lin1, Dinggang Shen1, and Pew-Thian Yap1
1University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, 2Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates

We present a sampling and reconstruction scheme that, when combined with multi-band imaging, accelerates dMRI acquisition by as much as 50 folds. In contrast to the conventional approach of acquiring a full diffusion-weighted (DW) volume for each diffusion wavevector, we acquire for each repetition time (TR) a volume consisting of interleaved slice groups, each corresponding to a different diffusion wavevector. This in effect results in a subsample of slices for each diffusion wavevector, based on which we can recover the full volumes for all wavevectors using a graph convolutional neural network (GCNN).

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