journal article·2023-09-03

Sparse-view CT reconstruction method for in-situ non-destructive testing of reinforced concrete

Wenju Peng, Yongshun Xiao

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Abstract

X-ray CT is an important in-situ non-destructive testing method that plays an important role in both production and materials research. In-situ CT of concrete can acquire images of concrete slices in the state of holding force loading, which is meaningful for the study of concrete crack development characteristics. However, existing X-ray CT equipment either cannot penetrate large reinforced concrete due to energy limitations or does not address the extremely sparse angular reconstruction problem associated with in-situ testing. In this paper, a sinogram Transformer method based on a priori projection data is proposed to solve this extremely sparse data complementation problem on a large-scale electron accelerator X-ray in-situ CT system. The simulated concrete sparse-view projection data and the full projection data are used as the input and label of the proposed method in order to experiment the method. The results demonstrate the feasibility of using a priori projected sine diagrams with ultra-sparse sine diagram residuals to track sine diagram variation, and verify that structures such as cracks and reinforcement, which dominate the deformation residuals of sinograms in concrete sections, can also be successfully recovered using Transformer in the ultra-sparse view projection of views 4–8.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Geophysical Methods and Applications · Non-Destructive Testing Techniques · Advanced X-ray and CT Imaging

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Engineering Physics, Tsinghua University, Beijing, China

Ministry of Education, Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Beijing, China

Funding

National Natural Science Foundation of China

References (38)

Show source-provided references
1. 10.1016/j.compstruct.2021.11365910.1016/j.compstruct.2021.113659 ↗
2. Gupta M, Khan MA, Butola R. Advances in applications of non-destructive testing (ndt): a review. Adv Mater Process Technol. 2021;8(2):2286–2307.
3. 10.1177/168781402091376110.1177/1687814020913761 ↗
4. 10.3390/app1106275010.3390/app11062750 ↗
5. 10.1088/1361-6501/ac354a10.1088/1361-6501/ac354a ↗
6. 10.3390/ma1408185910.3390/ma14081859 ↗
7. 10.1007/s11340-018-0427-y10.1007/s11340-018-0427-y ↗
8. 10.1177/0021998321105070510.1177/00219983211050705 ↗
9. 10.1364/AO.32.00373610.1364/AO.32.003736 ↗
10. 10.1117/12.93450910.1117/12.934509 ↗
11. 10.1117/12.60072210.1117/12.600722 ↗
12. Kudo H, Suzuki T, Rashed EA. Image reconstruction for sparse-view ct and interior ct-introduction to compressed sensing and differentiated backprojection. Quant Imaging Med Surg. 2013;3(3):147–161.
13. 10.1088/0031-9155/53/17/02110.1088/0031-9155/53/17/021 ↗
14. Sidky EY, Kao CM, Pan X. Study of an adaptive bolus chasing CT angiography. J Xray Sci Technol. 2006;14(1):27–38.
15. Shi L, Qu G, Zhao Y. Ultra-limited-angle ct image reconstruction algorithm based on reweighting and edge-preserving. J Xray Sci Technol. 2022;1:1–13.
16. 10.1118/1.283642310.1118/1.2836423 ↗
17. 10.1109/EMBC.2016.759159110.1109/EMBC.2016.7591591 ↗
18. 10.1109/TNS.2018.288894810.1109/TNS.2018.2888948 ↗
19. 10.1109/ISBI45749.2020.909862210.1109/ISBI45749.2020.9098622 ↗
20. 10.1109/TIP.2017.271309910.1109/TIP.2017.2713099 ↗
21. 10.1109/TMI.2018.282376810.1109/TMI.2018.2823768 ↗
22. 10.1109/TMI.2018.280569210.1109/TMI.2018.2805692 ↗
23. 10.1109/TMI.2018.282333810.1109/TMI.2018.2823338 ↗
24. 10.1109/TRPMS.2018.286761110.1109/TRPMS.2018.2867611 ↗
25. 10.48550/arXiv.1804.0428910.48550/arXiv.1804.04289 ↗
26. 10.1145/3503161.354820410.1145/3503161.3548204 ↗
27. 10.48550/arXiv.2111.1079010.48550/arXiv.2111.10790 ↗
28. 10.2139/ssrn.399108710.2139/ssrn.3991087 ↗
29. 10.1109/TMI.2021.307806710.1109/TMI.2021.3078067 ↗
30. 10.1088/1361-6420/aba41510.1088/1361-6420/aba415 ↗
31. 10.1117/12.264642810.1117/12.2646428 ↗
32. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing Systems, Red Hook, NY, USA. NIPS'17. Curran Associates Inc.; 2017. p. 6000–6010.
33. 10.1007/978-3-319-46493-0_3810.1007/978-3-319-46493-0_38 ↗
34. Xiong R, Yang Y, He D, et al. On layer normalization in the transformer architecture. 37th International Conference on Machine Learning, ICML 2020; 2020. p. 10524–10533.
35. 10.48550/arXiv.2003.0488710.48550/arXiv.2003.04887 ↗
36. 10.48550/arXiv.2104.1275310.48550/arXiv.2104.12753 ↗
37. 10.1109/CVPR42600.2020.0077910.1109/CVPR42600.2020.00779 ↗
38. 10.1016/j.conbuildmat.2019.07.19010.1016/j.conbuildmat.2019.07.190 ↗