The metrics shown in Table show that SD2I outperforms all other approaches but, as discussed previously, visual inspection and assessment of the reconstructed images is more important as there is no real ground truth image available for the experimental data. This is another advantage of the network compared to iterative approaches such as SART, SIRT and CGLS where there is no standard loss function one can use to calculate the optimal number of iterations (convergence criterion), especially when trying to reconstruct different datasets. The visual results clearly demonstrate that the SD2I reconstructions are considerably better quality than all other methods (FBP, CGLS, SART and SIRT) on the undersampled sinogram. Finally, it should be noted that, although the images have been normalised for better visualisation presentation, the SD2I, in contrast to other neural network reconstruction methods such as the GANrec, maintains the absolute intensity information which is essential in chemical tomography methods, such as XRD-CT. We tested two more experimental XRD-CT images with SD2Iu, which are shown in Figure and S10 with the metrics calculated in Table and S13. Figure and Table present the results from the reconstructions of synchrotron X-ray micro-CT data acquired from the same 10440 NMC532 Li-ion battery corresponding to two different crosssections. These two sinograms correspond to two different positions along the length of the battery (Figure ); in position (a) only the Cu current collector is primarily visible in the battery jelly roll while in position (b) the NMC532 cathode can also be observed. As with the XRD-CT data above, ground-truth is obtained by CGLS of a full projection and the sinogram is then decreased to ¼ of the original size and reconstructions obtained with FBP, SD2I as well as the SIRT, CGLS and SART iterative methods. The hyperparameters for the SD2I networks used in this work for the micro-CT data were kept the same for all datasets and no tweaking was required (initial learning rate of 0.001 with a decaying rate and a safe margin of 8000 epochs).