Many clinical MRI applications in chest and abdomen require low sensitivity to motion. In addition, high acquisition speed is necessary for imaging in non-cooperative patients or those unable to perform breath holds. These applications would benefit from the highly accelerated radial acquisition. Deep learning has been shown to provide good results for image reconstruction from highly under-sampled k-space data. Here we introduce a Projection GAN - a generative adversarial neural network, which is trained to reconstruct highly accelerated MR images from uniformly rotated projections. Our results show that even with aggressive under-sampling the reconstruction has great overall performance.