A scalable neural network architecture for self-supervised tomographic image reconstruction

Dong, Hongyang; Jacques, Simon D. M.; Kockelmann, W.; Price, Stephen J.; Emberson, Robert; Matras, Dorota; Odarchenko, Yaroslav; Giokaris, Athanasios; Gutowski, Olof; Dippel, Ann‐Christin; Zimmermann, M. v.; Beale, Andrew M.; Butler, Keith T.; Vamvakeros, Antonis

doi:10.1039/d2dd00105e

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2023

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“…Dong et al . devised a lightweight, CNN called the SingleDigit2Image (SD2I) network capable of reconstructing a tomographic image from a sinogram [ 169 ]. Its parametric reduction allows the network to scale more efficiently with increasing sinograms for XDS-CT.…”

Section: Recent Insights From Xds-ctmentioning

confidence: 99%

Recent developments in X-ray diffraction/scattering computed tomography for materials science

Omori,

Bobitan,

Vamvakeros

et al. 2023

Phil. Trans. R. Soc. A.

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X-ray diffraction/scattering computed tomography (XDS-CT) methods are a non-destructive class of chemical imaging techniques that have the capacity to provide reconstructions of sample cross-sections with spatially resolved chemical information. While X-ray diffraction CT (XRD-CT) is the most well-established method, recent advances in instrumentation and data reconstruction have seen greater use of related techniques like small angle X-ray scattering CT and pair distribution function CT. Additionally, the adoption of machine learning techniques for tomographic reconstruction and data analysis are fundamentally disrupting how XDS-CT data is processed. The following narrative review highlights recent developments and applications of XDS-CT with a focus on studies in the last five years. This article is part of the theme issue 'Exploring the length scales, timescales and chemistry of challenging materials (Part 2)'.

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Section: Recent Insights From Xds-ctmentioning

confidence: 99%

Recent developments in X-ray diffraction/scattering computed tomography for materials science

Omori,

Bobitan,

Vamvakeros

et al. 2023

Phil. Trans. R. Soc. A.

Self Cite

View full text Add to dashboard Cite

show abstract

A scalable neural network architecture for self-supervised tomographic image reconstruction

Abstract: We present a lightweight and scalable artificial neural network architecture which is used to reconstruct a tomographic image from a given sinogram. A self-supervised learning approach is used where the...

Cited by 1 publication

References 56 publications

Recent developments in X-ray diffraction/scattering computed tomography for materials science

Recent developments in X-ray diffraction/scattering computed tomography for materials science

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