In addition to the phantom dataset presented in the previous section, the efficacy of the method was evaluated with a second experimental dataset. Specifically, a dataset acquired from a commercially available and industrially relevant 10440 NMC532 Li-ion battery was used . This dataset consisted of 521 translation steps, 1000 projections and 1800 channels of sinograms. To train this big dataset, we divided it into batches of 55 channels, and each batch took ca. 8 h to process using 5000 epochs. To address this large dataset, we first selected an XRD diffraction peak from the Cu phase ((111) reflection) and reconstructed only the images without parallax corresponding to this peak within the 55-channel range. Then, we performed Rietveld analysis on this 521 x 521 x 55 XRD-CT dataset to get the chemical information of the Cu phase presented in this Li-ion battery dataset. The original dataset was performed using a 0-360 °scan range, but for testing the ParallaxNet, we only used the part of the data corresponding to the 0-180 °scan range so for each batch, the size of the reference sinogram was 521 x 500 x 55. For a comparison, the XRD-CT data were also reconstructed using the FBP algorithm using both the 180 and 360 °ranges and were analysed using the Rietveld method i.e. on the 55 selected channels of the Cu XRD peak (hkl reflection (111)). The ParallaxNet also utilized the FBP with 180 o projections to pre-train the generator for faster convergence.