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Abstract #1055

Concept of a symmetry-guided single-layer neural network for image reconstruction of undersampled radial-MRI k-space data.

Sjoerd Ypma1, Ivo Maatman1, Matthan Caan2, Dimitris Karkalousos2, Marnix Maas1, and Tom Scheenen1
1Radboud UMC, Radiology and Nuclear Medicine, University of Nijmegen, Nijmegen, Netherlands, 2Amsterdam UMC, Biomedical Engineering and Physics, University of Amsterdam, Amsterdam, Netherlands


Undersampled k-space data reconstruction results in aliasing artifacts. Compressed sensing theory enables image reconstruction by using a priori knowledge in the form of regularization. Increasingly, Machine Learning methods are used to learn the regularization from data itself, but these methods can result in unstable reconstructions.

We propose a translation equivariant single-layer neural network for reconstruction of radially measured k-space data. By exploiting translation symmetry, it can learn from randomly simulated data while still being applicable to in-vivo measurements. We tested robustness to small perturbations and reliability of the reconstruction of unexpected objects.

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