A striking advantage of many deep learning based reconstruction approaches, when compared to traditional methods, such as FBP, is their ability to achieve high quality reconstructions when only challenging data are practically available. These can be sinograms with angular undersampling, low signal-to-noise ratio or incomplete sinograms (e.g. not covering the full 0-180° angular range) . However, most of the approaches are applied on the FBP reconstructed images (i.e. post-processing of the reconstructed images) rather than performing directly the tomographic reconstruction and importantly rely on supervised learning which assumes a) that artefact-free images (labelled data) are available and b) that the networks can generalise (e.g. train with non-scientific datasets typically used for developing neural networks and yield high quality images when applied to experimental data). Unfortunately, these assumptions are rarely valid and the applicability of such networks to real experimental data is limited at best. Here, we show that the SD2I, apart from its ability to reconstruct large tomographic images in a selfsupervised manner, is able to suppress the angular undersampling artefacts while performing the tomographic reconstruction. In Figure , we show the reconstruction of the Shepp-Logan phantom with a severe angular-undersampling where we only have less than ¼ of the original sinogram projections (projections corresponding to ¼ of the detector elements). For comparison, also shown are the results obtained from the most often used iterative algorithms (SART, CGLS and SIRT) using the astra toolbox as well as from GANrec. Compared to all conventional reconstruction algorithms tested SD2I produces results with significantly fewer artefacts and much closer to the ground truth reconstruction. Importantly, it is clearly shown that the SD2Iu networks, which correspond to the smallest possible networks in terms of number of parameters, yield the best results. The use of the upsampling convolution layers actually improves the quality of the reconstruction, performing a function similar to denoising on the resultant images. It should be noted though that the network does not denoise the reconstructed images, it removes the angular undersampling artefacts. It therefore requires projection/sinogram data with high signal-to-noise ratio; it does not lead to higher quality reconstructed images than the FBP algorithm when the signal-to-noise ratio is low. show that this approach can be applied to larger image reconstruction tasks and the performance gains remain for SD2I. For calculating the SSIM and PSNR, we used the maximum possible pixel value as 1. A larger Shepp-Logan phantom image (512x512) was also tested (sinogram with size equal to 512x128) and the SD2I results are presented in Figure and Table . The impact of the loss function is shown in Table . It should be noted here that the results for the various metrics strongly depend on the choice of the ground truth image. This is not an issue for the Shepp-Logan phantom but it is a problem for the experimental data where there is no ground truth image available. This means that the quality of the reconstructed images has to be done primarily through visual inspection as the results from the various metrics can be misleading. To illustrate this problem, we measured the performance of SD2I as well as FBP, SART, SIRT and CGLS using different images as the ground truth image for the Shepp-Logan image (Figure and Tables ). If the FBP reconstructed image using the full projection set (400 projections) is used as the ground truth, then the metrics suggest that SIRT and CGLS outperform the SD2I. However, this is clearly not the case as shown in Figures and and from the fact that the clean (real ground truth) Shepp-Logan phantom image shows worse results for all metrics (Table ). The result obtained with the CGLS method using the full projection set (400 projections) looks closer to the ground truth image compared to the FBP, SART and SIRT results obtained using the full projection set and for this reason it is used as the ground truth for evaluating the performance of the SD2I network for the experimental data. Finally, it is important to note that when the clean Shepp-Logan image (real ground truth) or the CGLS image obtained using the full projection set are used as the ground truth, it can be seen that the SD2I with less than ¼ projections (64 projections) outperforms FBP, SART and SIRT reconstructions using the full projection set (400 projections). This result further illustrates the accuracy of the SD2I reconstructions and the potential of this new network for data exhibiting angular undersampling.