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Unsupervised deep basis pursuit: Learning reconstruction without ground-truth data

Proceedings of the 27th Annual Meeting of ISMRM, (2019)

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Basis pursuit is a compressed sensing optimization in which the l1-norm is minimized subject to model error constraints. Here we use a deep neural network prior instead of l1-regularization. Using known noise statistics, we jointly learn the prior and reconstruct images without access to ground-truth data. During training, we use alternat...更多

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Jonathan I Tamir
Jonathan I Tamir
Michael Lustig
Michael Lustig
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