• Firstly, ParallaxNet operates without requiring a priori knowledge about the chemical composition of the sample being measured. • DLSR also requires the identification of all phases and the construction of a robust physical model; this can potentially lead to some minor components being overlooked during the inspection of diffraction patterns in the sinogram data. • Furthermore, ParallaxNet is more scalable. In this work, we applied the ParallaxNet on the full size experimental phantom XRD-CT datasets. However, the DLSR can only be applied on the scaled-down version of the same dataset(s) as shown in the DLSR paper (e.g. with 121 x 121 image sizes for the Li ion battery). Moreover, ParallaxNet does not need as much RAM requirements as the DLSR approach, especially when DLSR is used in conjunction with TOPAS version 7.