The hyperparameters for the SD2I networks used in this work for the XRD-CT data were kept the same for all datasets and no tweaking was required (initial learning rate of 0.0005 with a decaying rate and a safe margin of 6000 epochs). It can be clearly seen that both the visual inspection and the metrics shown in Table indicate that the SD2I performed the best among all the conventional methods we tested. The magnified region in Figure also shows that SD2I is able to retain very fine features present in the images, in this case corresponding to the channels and network of the 3D printed catalyst. In Figure we show results from another XRD-CT dataset, using two larger sinograms selected from two diffraction peaks of interest (i.e. NMC532 and Cu phases respectively). This XRD-CT dataset was acquired using a commercially available 10440 NMC532 Li-ion battery . The ground truth image was obtained using the CGLS algorithm on the 547 x 400 sinograms which already have fewer projections (i.e. 400 projections) than the Nyquist sampling theorem dictates (i.e. /2 x 547). All the reconstruction algorithms and neural networks were tested using 547 x 100 sinograms which are severely undersampled data. As shown in Figure , both reconstructed images indicate that the SD2I reconstructions have suppressed the angular undersampling artefacts while these are clearly present in the traditional methods.