We have presented a new, self-supervised, deep-learning approach for tomographic reconstruction. The SD2I approach uses a generator network to produce a sample image, which is then converted to a sinogram via the Radon transform; the parameters of the network are updated by backpropagation to minimise the difference between the sample sinogram and the sinogram produced by the network. Similar to other deep-learning reconstruction approaches, our SD2I approach is much more robust to angular undersampling than traditional reconstruction approaches. However, SD2I is also considerably more computationally efficient than other deeplearning reconstruction methods. This means the SD2I can be applied to much larger sinograms and can produce results with a significantly lighter hardware requirement than other deep-learning approaches. The advantages of the new architecture can be summarised as the following: