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Abstract #4778

Deep transform networks for scalable learning of MR reconstruction

Anatole Moreau1,2, Florent Gbelidji1,3, Boris Mailhe1, Simon Arberet1, Xiao Chen1, Marcel Dominik Nickel4, Berthold Kiefer4, and Mariappan Nadar1

1Digital Services, Digital Technology & Innovation, Siemens Medical Solutions, Princeton, NJ, United States, 2EPITA, Le Kremlin-BicĂȘtre, France, 3CentraleSupĂ©lec, Gif-sur-Yvette, France, 4Siemens Healthcare, Application Development, Erlangen, Germany

In this work we introduce RadixNet, a fast, scalable, transform network architecture based on the Cooley-Tukey FFT, and use it in a fully-learnt iterative reconstruction with a residual dense U-Net image regularization. Results show that fast transform networks can be trained at 256x256 dimensions and outperform the FFT.

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