Keywords: Machine Learning/Artificial Intelligence, Image ReconstructionMR Image reconstruction of uniformly undersampled data often relies on prior information estimated from additional calibration data, leading to compromised acquisition efficiency and flexibility. Here, we propose a joint multi-slice deep learning strategy for MR image reconstruction from uniformly undersampled data with complementary undersampling across adjacent slices. Specifically, we design a slice fusion block to fully exploit the structural and phase similarity in adjacent slices and a slice shift block to further suppress the aliasing artifacts introduced by uniform undersampling. Consequently, the proposed strategy enables accurate MR image reconstruction for both image magnitude and phase without additional calibration information.
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