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Abstract #0469

Exploring Complex-Valued Neural Networks with Trainable Activation Functions for Magnetic Resonance Imaging

Guillaume Daval-Frerot1,2, Xiao Chen1, Simon Arberet1, Boris Mailhé1, Peter Speier3, Mathias Nittka3, Heiko Meyer3, and Mariappan S Nadar1

1Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, United States, 2EPITA, Le Kremlin-Bicêtre, France, 3Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany

MR signals by nature are complex valued. However, most of the current deep neural networks for MR are derived from applications dealing with real-valued images. Recent studies proposed an adaptation of neural networks to the complex domain to learn a better representation of the signal. In this study, multiple CVNN with trainable complex-valued activation functions are proposed and validated on MR fingerprinting regression problem. 2D activation functions with trainable parameters have been demonstrated here to suit the CVNN well and provide significant improvement over the non-trainable versions.

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