We propose an ensembled Ʃ-net for fast parallel MR image reconstruction, including parallel coil networks, which perform implicit coil weighting, and sensitivity networks, involving explicit sensitivity maps. The networks in Ʃ-net are trained with various ways of data consistency, i.e., gradient descent, proximal mapping, and variable splitting, and with a semi-supervised finetuning scheme to adapt to the k-space data at test time. We achieved robust and high SSIM scores by ensembling all models to a Ʃ-net. At the date of submission, Ʃ-net is the leading entry of the public fastMRI multicoil leaderboard.
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