A neural network reconstruction approach for obtaining parallax-free X-ray powder diffraction computed tomography data from large samples

In this study, we introduce an innovative method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples. Our approach integrates a unique 3D neural network architecture with a forward projector that accounts for the experimental geometry. This self-supervised technique for tomographic volume reconstruction is designed to be chemistry-agnostic, eliminating the need for prior knowledge of the sample's chemical composition. We showcase the efficacy of this method through its application on both simulated and experimental X-ray diffraction tomography data, acquired from a phantom sample and a commercially available and industrially relevant NMC532 cylindrical Lithium-ion battery.