In this abstract, we present a proof-of-concept method for effective diffusion MRI reconstruction from slice-undersampled data. Instead of acquiring full diffusion-weighted (DW) image volumes, only a subsample of equally-spaced slices are acquired. We show that the complementary information from DW volumes corresponding to different diffusion wavevectors can be harnessed using graph convolutional neural networks for reconstruction of the full DW volumes.
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