We start by comparing the performance of our new architecture against the filtered back projection (FBP) algorithm and other neural network based reconstruction algorithms. For this comparison we use a sinogram created using the Shepp-Logan phantom with image size of 256 x 256 pixels; the sinogram size is 256 x 400 pixels, corresponding to detector elements and number of projections respectively. The reconstructed images are presented in Figure while Table compares the results from the various reconstruction methods applied, using several common image quality metrics and specifically the mean absolute error (MAE), mean squared error (MSE), structural similarity index measure (SSIM) and peak signal to noise ratio (PSNR). We find that all variants of our SD2I architecture outperform both GANrec and FBP across all metrics. The SD2I architectures that perform best are those where the convolutional part of the network is a single size, rather than including upsampling layers. However, even the SD2I architecture with upsampling convolutional layers (SD2Iu) (which has significantly fewer parameters than that without upsampling) performs very well. We also find that changing the size of the final dense layer in the SD2I architecture (the factor k) has a small but appreciable effect on the image quality. Somewhat surprisingly, of the architectures with no upsampling layers (SD2I), the one with the smaller final dense layer performs slightly better, this could be due to local minima trapping in the larger network. Nonetheless, the main point is that SD2I performs very well on the standard Shepp-Logan phantom, regardless of architecture hyperparameters (within a reasonable range). Adam was used as the optimisation algorithm , a combined MAE and SSIM loss function was used with the following formula: