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A deep learning-based super-resolution workflow applied to X-ray tomography enables accurate 3D quantification of defects like porosity and surface roughness in multiscale lattice structures while reducing scan time by several orders of magnitude. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Super-resolution X-ray tomography using deep learning applied to the 3D quantification of defects in lattice structures

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