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

Jonathan I Tamir
Jonathan I Tamir
Michael Lustig
Michael Lustig

Proceedings of the 27th Annual Meeting of ISMRM, 2019.

Cited by: 4|Bibtex|Views17|Links

Abstract:

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...More

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