Abstract #1186

The role of nonlinear activations in Fourier-domain neural networks: Noise resilience, regularization, blurring and autocorrelation artifact

Peter Dawood^1,2, Felix Breuer³, Istvan Homolya⁴, Maximilian Gram², Peter M. Jakob², Moritz Zaiss^1,5, and Martin Blaimer³

¹Institute of Neuroradiology, University Hospital Erlangen, Erlangen, Germany, ²Experimental Physics 5, University of Würzburg, Würzburg, Germany, ³Magnetic Resonance and X-ray Imaging Department, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Würzburg, Germany, ⁴Molecular and Cellular Imaging, Comprehensive Heart Failure Center, University Hospital Würzburg, Würzburg, Germany, ⁵5Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, RAKI, GRAPPA, Explainable AI, ReLU activation

Motivation: Extending explainable Artificial Intelligence to Fourier-domain deep neural networks.

Goal(s): To illuminate the impact of nonlinear activations in Fourier-domain neural networks for interpolation on noise resilience in image space.

Approach: An image space formalism for convolutional neural networks for Fourier-domain interpolation is used to inspect the activation part, which is linked to noise resilience in image space, in a human-readable manner. The nonlinearity-degree in the model is controlled via the negative slope parameter of leaky ReLU activation.

Results: The negative slope in non-linear ReLU activation may serve as regularization parameter, allowing to adjust tradeoff between image blurring, autocorrelation artifact and noise resilience.

Impact: This work illuminates the role of nonlinearity in robust artificial neural networks for Fourier-domain interpolation via activated convolution layers at limited training data and shows possible implications of noise resilience, image blurring and autocorrelation artifacts in the image center.

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