Here we used =0.84 for all simulated Shepp-Logan images. The learning rate was set to 0.0005 for networks presented in this work. The learning rate was automatically reduced during training if the loss function was not decreasing after 300 iterations using a downscaling factor of 0.5 (Tensorflow ReduceLROnPlateau implementation ); 6000 epochs were used during the image reconstruction process. It is important to note here that the various metrics provide only an indication of the image quality reconstruction and one should always inspect the resulting images regardless of the values of the various metrics. Reconstruction times are presented in Tables . To demonstrate the enhancement in the quality of the reconstructed image as a result of the single digit input, we assessed the performance of the networks, specifically the SD2I and SD2Iu models, starting from the last fully connected layer. When the networks were provided with a 64unit vector of ones (the same size as the fully connected layer preceding the final layer), the results were markedly poorer and could not compete with the SD2Iu in terms of reconstructing the 256x64 size sinogram. The results are presented in Figure .