We use a joint loss function with the mean squared error (MSE) and the structural similarity index measure (SSIM) in this architecture. The loss function compares the real experimental sinograms with the generated sinograms and updates the weights in the generator to give a generated sinogram volume that resembles better the experimental sinogram volume on the iteration. Normally, the architecture can yield high quality reconstructions after 1000 iterations. We tested various loss functions, including MSE, mean absolute error (MAE), SSIM, and a joint loss function that combines MSE and SSIM. These tests were conducted with ParallaxNet on the simulated XRD-CT dataset used in this work. The results are presented in Supplementary Table . Based on these findings, we chose the joint loss function that combines MSE and SSIM as the loss function to be used for ParallaxNet. The reconstruction process of this self-supervised method can be expressed as: