Overall, the network starts with a single neuron and yields a 2D image with size of (m, m) which is equal to the image size obtained with the conventional tomographic reconstruction algorithms. The SD2Iu's architecture allows for radical decrease in the number of parameters and allows it to reconstruct images that are more than 1028 × 1028 large. While several deep learning approaches have proved very successful for CT reconstruction, a major barrier to their widespread adoption is that the number of parameters (and hence required computational resources) scales poorly as the size of sinogram increases. The new architecture that we propose, has at least an order of magnitude fewer parameters than existing deep learning approaches (e.g. Automap and GANrec), as shown in Figure and Table . Note that for these tests it was not possible to use Automap on images larger than 128 x 128 pixels, due to memory constraints.