Algorithm for Optimizing the Parameters of Sandwich X-ray Detectors
Source abstract
Abstract
Abstract We describe an algorithm for optimizing the parameters of sandwich X-ray detectors in relation to the recognition of materials by the dual energy method. The application of the algorithm is illustrated by a concrete example in which we obtain the thicknesses of the first detector and the intermediate filter (that are part of a sandwich detector) that are optimal for detecting explosives for the given materials of the first detector (CsI) and the intermediate filter (Cu).
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced X-ray and CT Imaging · Medical Imaging Techniques and Applications · Radiation Dose and Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (26)
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1. Khan, S.U., Khan, I.U., Ullah, I., Saif, N., and Ullah, I., A review of airport dual energy X-ray baggage inspection techniques: Image enhancement and noise reduction, J. X-ray Sci. Technol., 2020, vol. 28, no. 3, pp. 481–505. https://doi.org/10.3233/XST-20066310.3233/XST-200663 ↗
2. Yalçın, O. and Reyhancan, I.A., Detection of explosive materials in dual-energy X-ray security systems, Nucl. Instrum. Meth. Phys. Res. Sect. A, 2022, vol. 1040, p. 167265. https://doi.org/10.1016/j.nima.2022.16726510.1016/j.nima.2022.167265 ↗
3. Chang, C.H., Ni, Y.C., and Tseng, S.P., Calculation of effective atomic numbers using a rational polynomial approximation method with a dual-energy X-ray, J. X-ray Sci. Technol., 2021, vol. 29, no. 2, pp. 317–330. https://doi.org/10.3233/xst-20079010.3233/xst-200790 ↗
4. Yim, C.W. and Hong, S.G., A MCNP simulation for a new dual-energy dual-beam X-ray inspection method using multi-angle compton scattering to determine the effective atomic number of explosives, Radiat. Phys. Chem., 2022, vol. 195, p. 110084. https://doi.org/10.1016/j.radphyschem.2022.11008410.1016/j.radphyschem.2022.110084 ↗
5. Duvillier, J., Dierick, M., Dhaene, J., Van Loo, D., Masschaele, B., Geurts, R., Hoorebeke, L.V., and Boone, M.N., Inline multi-material identification via energy radiographic measurements, NDT & E Int., vol. 94, pp. 120–125. https://doi.org/10.1016/j.ndteint.2018.01.00210.1016/j.ndteint.2018.01.002 ↗
6. Cordova, A., Technologies for primary screening in aviation security, J. Transp. Secur., 2022, vol. 15, nos. 3–4, pp. 141–159. https://doi.org/10.1007/s12198-022-00248-810.1007/s12198-022-00248-8 ↗
7. Linardatos, D., Koukou, V., Martini, N., Konstantinidis, A., Bakas, A., Fountos, G., Valais, I., and Michail, C., On the response of a micro non-destructive testing X-ray detector, Materials, 2021, vol. 14, p. 888. https://doi.org/10.3390/ma1404088810.3390/ma14040888 ↗
8. Vukadinovic, D. and Anderson, D., X-Ray Baggage Screening and Artificial Intelligence (AI), Luxembourg: Publications Office of the European Union, 2022. https://doi.org/10.2760/4636310.2760/46363 ↗
9. Osipov, S.P., Udod, V.A., and Wang, Y., Identification of materials in X-ray inspections of objects by the dualenergy method, Russ. J. Nondestr. Test., 2017, vol. 53, no. 8, pp. 568–587. https://doi.org/10.1134/S106183091708005810.1134/S1061830917080058 ↗
10. Kayalvizhi, R., Malarvizhi, S., Topkar, A., and Vijayakumar, P., Raw data processing techniques for material classification of objects in dual energy X-ray baggage inspection systems, Radiat. Phys. Chem., 2022, vol. 193, p. 109512. https://doi.org/10.1016/j.radphyschem.2021.10951210.1016/j.radphyschem.2021.109512 ↗
11. Mamchur, D., Peksa, J., Le Clainche, S., and Vinuesa, R., Application and advances in radiographic and novel technologies used for non-intrusive object inspection, Sensors, 2022, vol. 22, no. 6, p. 2121. https://doi.org/10.3390/s2206212110.3390/s22062121 ↗
12. Busi, M., Kehres, J., Khalil, M., and Olsen, U.L., Effective atomic number and electron density determination using spectral X-ray CT, in: Anomaly Detection and Imaging with X-rays (ADIX) IV, SPIE Defense Commer. Sens., Baltimore, 2019, vol. 10999, pp. 7–17. https://doi.org/10.1117/12.251985110.1117/12.2519851 ↗
13. Jumanazarov, D., Koo, J., Busi, M., Poulsen, H.F., Olsen, U.L., and Iovea, M., System-independent material classification through X-ray attenuation decomposition from spectral X-ray CT, NDT & E Int., 2020, vol. 116, p. 102336. https://doi.org/10.1016/j.ndteint.2020.10233610.1016/j.ndteint.2020.102336 ↗
14. Iovea, M., Neagu, M., Duliu, O.G., Oaie, G., Szobotka, S., and Mateiasi, G., A dedicated on-board dual-energy computer tomograph, J. Nondestr. Eval., 2011, vol. 30, pp. 164–171. https://doi.org/10.1007/s10921-011-0104-x10.1007/s10921-011-0104-x ↗
15. Smith, R.C. and Connelly, J.M., CT technologies, in: Counterterrorist Detection Techniques of Explosives, Amsterdam: Elsevier, 2022, pp. 29–45. https://doi.org/10.1016/B978-0-444-64104-5.00009-610.1016/B978-0-444-64104-5.00009-6 ↗
16. Alvarez, R.E., Invertibility of the dual energy X-ray data transform, Med. Phys., 2019, vol. 46, no. 1, pp. 93–103. https://doi.org/10.1002/mp.1325510.1002/mp.13255 ↗
17. Osipov, S., Chakhlov, S., Udod, V., Usachev, E., Schetinkin, S., and Kamysheva, E., Estimation of the effective mass thickness and effective atomic number of the test object material by the dual energy method, Radiat. Phys. Chem., 2020, vol. 168, p. 108543. https://doi.org/10.1016/j.radphyschem.2019.10854310.1016/j.radphyschem.2019.108543 ↗
18. Zhang, Y., Kong, W., Li, D., and Liu, X., On using XMC R-CNN model for contraband detection within X-ray baggage security images, Math. Probl. Eng., 2020, vol. 2020, p. 1823034. https://doi.org/10.1155/2020/182303410.1155/2020/1823034 ↗
19. Alvarez, R.E., Analytic models for spectral X-ray imaging, Preprint of Aprend Technology, 2019. https://doi.org/10.13140/RG.2.2.12391.0912810.13140/RG.2.2.12391.09128 ↗
20. Fredenberg, E., Spectral and dual-energy X-ray imaging for medical applications, Nucl. Instrum. Phys. Res., Sect. A, 2018, vol. 878, pp. 74–87. https://doi.org/10.1016/j.nima.2017.07.04410.1016/j.nima.2017.07.044 ↗
21. Zhao, S., Pan, H., Zhang, W., Xia, D., and Zhao, X., An oblique projection modification technique (OPMT) for fast multispectral CT reconstruction, Phys. Med. & Biol., 2021, vol. 66, no. 6, p. 065003. https://doi.org/10.1088/1361-6560/abe02810.1088/1361-6560/abe028 ↗
22. Udod, V.A., Osipov, S.P., and Nazarenko, S.Yu., Algorithm for evaluating errors in recognition of materials in X-ray testing system containing X-ray sandwich detectors, Russ. J. Nondestr. Test., 2022, vol. 58, no. 1, pp. 46–56. https://doi.org/10.1134/S106183092201006510.1134/S1061830922010065 ↗
23. Udod, V.A., Vorobeichikov, S.E., and Nazarenko, S.Yu., Mathematical models of radiation transparency of test objects when using sandwich X-ray radiation detectors, Russ. J. Nondestr. Test., 2020, vol. 56, no. 2, pp. 161–170. https://doi.org/10.1134/S106183092002009610.1134/S1061830920020096 ↗
24. Udod, V.A., Osipov, S.P., and Wang, Y., Estimating the influence of quantum noises on the quality of material identification by the dual-energy method, Russ. J. Nondestr. Test., 2018, vol. 54, no. 8, pp. 585–600. https://doi.org/10.1134/S106183091808007710.1134/S1061830918080077 ↗
25. Slavashevich, I., Pozdnyakov, D., Kasiuk, D., and Linev, V., Optimization of physico-topological parameters of dual energy X-ray, Eng. Scintill. Mater. Radiat. Technol., Selected Articles of ISMART 2018 (Minsk, 2019), vol. 227, p. 262. https://doi.org/10.1007/978-3-030-21970-3_1910.1007/978-3-030-21970-3_19 ↗
26. Liang, K.J., Sigman, J.B., Spell, G.P., Strellis, D., Chang, W., Liu, F., Mehta, T., and Carin, L., Toward automatic threat recognition for airport X-ray baggage screening with deep convolutional object detection, Preprint of arXiv, 2019, p. 1912.06329. https://doi.org/10.48550/arXiv.1912.0632910.48550/arXiv.1912.06329 ↗