Abstract #4778

Deep transform networks for scalable learning of MR reconstruction

Anatole Moreau^1,2, Florent Gbelidji^1,3, 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-Bicêtre, France, ³CentraleSupélec, 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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