A neural network reconstruction approach for obtaining parallax-free X-ray powder diffraction computed tomography data from large samples
Hongyang Dong,
Simon D.M. Jacques,
Keith T. Butler
et al.
Abstract: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… Show more
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