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.