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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.

October 21, 2025Scientific ReportsOpen Access

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

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AKAntoine KlosInstitut polytechnique de Grenoble LSLuc SalvoInstitut polytechnique de Grenoble PLPierre LhuissierInstitut polytechnique de Grenoble

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Klos et al. (2025) studied this question.

synapsesocial.com/papers/6a1c8cc5ecffbcc5fca17ca1 https://doi.org/10.1038/s41598-025-20372-4

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October 21, 2025Scientific ReportsOpen Access

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

View Full Paper

Ask AI

Bookmark

Authors

AKAntoine KlosInstitut polytechnique de Grenoble LSLuc SalvoInstitut polytechnique de Grenoble PLPierre LhuissierInstitut polytechnique de Grenoble

Discussion

Member takes

Implication

Key Points

Key points are not available for this paper at this time.

Cite This Study

Klos et al. (2025) studied this question.

synapsesocial.com/papers/6a1c8cc5ecffbcc5fca17ca1 https://doi.org/10.1038/s41598-025-20372-4

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