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

Author:

Dong Hongyang1ORCID,Jacques Simon D. M.2ORCID,Kockelmann Winfried3ORCID,Price Stephen W. T.2ORCID,Emberson Robert4,Matras Dorota56ORCID,Odarchenko Yaroslav2ORCID,Middelkoop Vesna7ORCID,Giokaris Athanasios2,Gutowski Olof8,Dippel Ann-Christin8ORCID,von Zimmermann Martin8,Beale Andrew M.129ORCID,Butler Keith T.1011ORCID,Vamvakeros Antonis212ORCID

Affiliation:

1. Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK

2. Finden Ltd, Rutherford Appleton Laboratory, Building R71, Harwell, Oxford, OX11 0QX, UK

3. STFC, Rutherford Appleton Laboratory, ISIS Facility, Harwell, OX11 0QX, UK

4. Department of Mathematics & Statistics, Lancaster University, Bailrigg, Lancaster, LA1 4YW, UK

5. Diamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0DE, UK

6. The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot, OX11 0RA, UK

7. Flemish Institute for Technological Research (VITO), B-2400 Mol, Belgium

8. Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany

9. Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxon OX11 0FA, UK

10. SciML, Scientific Computing Department, STFC Rutherford Appleton Laboratory, Harwell Campus, Didcot, OX11 0QX, UK

11. School of Engineering and Materials Science, Queen Mary University of London, Mile End Rd, Bethnal Green, London E1 4NS, UK

12. Dyson School of Design Engineering, Imperial College London, London SW7 2DB, UK

Abstract

We present a lightweight and scalable artificial neural network architecture which is used to reconstruct a tomographic image from a given sinogram.

Funder

Engineering and Physical Sciences Research Council

Science and Technology Facilities Council

Royal Society

Publisher

Royal Society of Chemistry (RSC)

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Obtaining parallax-free X-ray powder diffraction computed tomography data with a self-supervised neural network;npj Computational Materials;2024-09-02

2. Recent developments in X-ray diffraction/scattering computed tomography for materials science;Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences;2023-09-11

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