Figure displays the lattice parameter maps obtained through Rietveld analysis. As seen in the FBP with the 180 °scan range lattice parameter map, the parallax artefact results in unevenly distributed lattice parameter values (as determined by the Rietveld analysis) across different positions of the same material (Cu phase). However, both the FBP with the 360 °scan range and the ParallaxNet results with 180 °scan range yield lattice parameter maps that are evenly distributed across all positions. The histogram depicting the distribution of lattice parameters for the three maps is shown in Figure . The mean values of the lattice parameters inside the signal area for the FBP with 360 °scan range, the FBP with 180 °scan range, and the ParallaxNet result are 3.6098 Å, 3.6088 Å, and 3.6103 Å, respectively. The scale factor maps and the R wp from the Rietveld analysis maps can be found in Supplementary Figures and It was therefore demonstrated that ParallaxNet can accurately reconstruct XRD-CT images/diffraction patterns of this experimental Li-ion dataset and that it is possible to extract meaningful chemical information from just a single peak of the XRD pattern. Subsequently, a broader range of diffraction channels was chosen, encompassing 555 out of the 1800 channels from the original dataset. These channels span a native 2θ value range from 1.203 to 4.877 °. We divided these 555 channels into 11 segments, each containing 55 channels, consistent with the phantom dataset approach. To mitigate edge effects between the reconstructed images of each batch, we incorporated a 5-pixel overlap on either side of each segment. We then averaged the overlapping sections to produce the final XRD-CT image volume with dimensions of 521 x 521 x 555. To reconstruct this expanded dataset, ParallaxNet required 90 h of training time, which includes both initialization and pre-training with FBP. Since each batch is independent, we utilized three NVIDIA Quadro RTX8000 GPUs to process these 11 batches in parallel using PyTorch. In the end, it took ca. 33 real-world h to complete this dataset. It's worth noting that this represents the most extreme scenario encountered in real-world experimental datasets, and the DLSR method cannot handle a dataset of this magnitude.