M. V. Chukalina, A. V. Buzmakov, A. S. Ingacheva, Ya. L. Shabelnikova, V. E. Asadchikov, I. N. Bukreeva, D. P. Nikolaev Analysis of the Tomographic Reconstruction from Polychromatic Projections for Objects with Highly Absorbing Inclusions
M. V. Chukalina, A. V. Buzmakov, A. S. Ingacheva, Ya. L. Shabelnikova, V. E. Asadchikov, I. N. Bukreeva, D. P. Nikolaev Analysis of the Tomographic Reconstruction from Polychromatic Projections for Objects with Highly Absorbing Inclusions

The method of computer tomography is used for studying the internal structure of an object without its physical destruction. If the object contains highly absorbing inclusions, the reconstructed image contains characteristic artifacts called "metal". Such distortions may conceal or simulate both pathologies in medical research and, for example, stress states or cracks in products in the case of industrial use of the method. The paper analyzes possible sources of artifacts. The results of the reconstruction of a baby tooth image measured on a laboratory microtomograph are discussed. The tooth was removed before the end of the root resorption process, which made it possible to strengthen the strongly absorbing particle in thecavity between the roots before the measurements. The presence of a strongly absorbing inclusion gave rise to artifacts in the reconstructed image. This work is devoted to the analysis of the possibility of reducing these artifacts. In addition to visual comparison of the results of reconstruction of the tooth cross-section without inclusion and the results of reconstruction in the presence of inclusion calculated values of metrics RMSE and SSIM. The obtained results show that application of algebraic approach allows improving the reconstruction quality in the presence of strongly absorbing inclusions.


computer tomography, polychromatic scanning, "metal" artifacts, algebraic recovery methods, nonlinear optimization, X-ray radiation.

DOI 10.14357/20718632200305

PP. 49-61.

1. Gordon R. and Herman G.T. Three-Dimensional Reconstruction from Projections: A Review of Algorithms. // International Review of Cytology. 1974. V. 38. P. 111-151. DOI: 10.1016/S0074-7696(08)60925-0.
2. Brooks R.A., De Chiro G. 1976. Beam hardaning in X-ray reconstructive tomography. // Phys. Med. Biol. V. 21 Num. 3. PP. 390-398. DOI: 10.1088/0031-9155/21/3/004.
3. Kalender W. A, Hebel R., Ebersberger J. Reduction of CT artifacts caused by metallic implants. // Radiology. 1987. 164(2): 576-577. DOI: 10.1148/radiology.164.2.3602406.
4. Luzhbin D., Wu J. Model Image-Based Metal Artifact Reduction for Computed Tomography. // J Digit Imaging. 2020. V. 33, Num. 1. PP. 71-82. DOI: 10.1007/s10278-019-00210-6.
5. Zopfs D., Lennartz S., Pennig L. et al. Virtual monoenergetic images and post-processing algorithms effectively reduce CT artifacts from intracranial aneurysm treatment. // Sci Rep. 2020. V. 10, Num. 6629/ PP. 1-10.
6. Pashina T. V. Gajdel' A., A., Zel'ter P. M., Kapishnikov A. V., Nikonorov A. V. Sravnenie algoritmov vydeleniya oblasti interesa na komp'yuternyh tomogrammah legkih [Automatic highlighting of the region of interest in computed tomography images of the lungs]. // Komp'yuternaya optika [Computer optics]. 2020. Т. 44. № 1. С.74-81. DOI:
7. Mellander H., Ramgren B., Ullberg T., Fransson V., Ydström K., Wasselius J. Brain dual energy computed tomography and intracranial coils – can the metal artifacts be reduced? // Congress: EuroSafe Imaging 2020. 2020. ESI-08910. DOI:10.26044/esi2020/ESI-08910.
8. Coleman M., Sinclair A. A beam-hardening correction using dualenergy computed tomography. // Physics in Medicine & Biology. 1985. V. 30, № 11. P 1251.
9. Herman, G. T. Correction for beam hardening in computed tomography. // Physics in Medicine and Biology. 1979. V. 24, № 1. P. 81.
10. Hammersberg P., Mangard M. Correction for beam hardening artefacts in computerised tomography. // Journal of X-ray Science and Technology. 1998. V. 8, № 1. P. 75—93.
11. Xie S., Zhuang W., Li B., Bai P., Shao W., Tong Yu. Blind deconvolution combined with level set method for correcting cupping artifacts in cone beam CT. // SPIE Medical Imaging. 2017. V. 10133. P. 010133z.
12. Pauwels R., Cao W., Wang B., Xiao Yo., Dewulf W. Exploratory research into reduction of scatter and beam hardening in industrial computed tomography using convolutional neural networks. // 9th International Conference on Industrial Computed Tomography. Issue 2019-3. 2019. P. 1-8.
13. Bamberg F., Dierks A., Nikolaou K., Reiser M. F., Becker C. R., Johnson T. R. C. Metal artifact reduction by dual energy computed tomography using monoenergetic extrapolation. // European Radiology. 2011. V. 21. Num. 7. P. 1424-1429. DOI: 10.1007/s00330-011-2062-1.
14. Anderla A., Sladojevic S., Delso G., Culibrk D., Mirković M., Stefanovic D. Suppression of metal artefacts in CT using virtual sinograms and corresponding MR images. // Current science. 2017. V. 112. Num. 7. P. 1505-1511. DOI: 10.18520/cs/v112/i07/1505-1511.
15. Oehler M., Buzug T.M. Statistical image reconstruction for inconsistent CT projection data. // Methods of information in medicine. 2007. V.3. P. 261-269. DOI: 10.1160/ME9041.
16. Gordon R. A Tutorial on ART (Algebraic Reconstruction Techniques) // IEEE Transactions on Nuclear Science. 1974. V.21. Num. 3. P. 78-93. DOI: 10.1109/TNS.1974.6499238.
17. Chukalina M. V., Ingacheva A., Prun V. E., Buzmakov A. V., Nikolaev D. P. A way to reduce the artifacts caused by intensely absorbing areas in computed tomography. // 29th European Conference on Modelling and Simulation. 2015. P. 527-531. DOI:10.7148/2015-0527.
18. Chukalina, M. V., Ingacheva, A. S., Buzmakov, A. V., Krivonosov, Y. S., Asadchikov, V. E., & Nikolaev, D. P. A Hardware and Software System for Tomographic Research: Reconstruction via Regularization //Bulletin of the Russian Academy of Sciences: Physics. 2019.V. 83(2). P. 150-154. DOI: 10.3103/S1062873819020084
19. Kak A. C., Slaney M. Principles of Computerized Tomographic Imaging. // Classics in applied mathematics. 1998. DOI: 10.1118/1.1455742.
20. Gilbert P. Iterative methods for the three-dimensional reconstruction of an object from projections. // J. Teor. Biol. 1972. V. 36. P. 105-117.
21. Chukalina M., Nikolaev D., Sokolov V., Ingacheva A., Buzmakov A., Prun V. CT metal artifact reduction by soft inequality constraints. // Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015). 2015. 98751C. DOI: 10.1117/12.2228810
22. Lehmann, E. L.; Casella, George (1998). Theory of Point Estimation (2nd ed.). New York: Springer. ISBN 978-0-387-98502-2. MR 1639875.
23. Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity" // IEEE Transactions on Image Processing, 2004 V. 13(4), P. 600–612, Apr.
24. Buzmakov, A. V., Asadchikov, V. E., Zolotov, D. A., Chukalina, M. V., Ingacheva, A. S., & Krivonosov, Y. S. (2019). Laboratory x-ray microtomography: Ways of processing experimental data. Bulletin of the Russian Academy of Sciences: Physics, 83(2), 146-149. DOI: 10.3103/S1062873819020060.
25. McDavid W.D., Waggener R.G., Payne W.H., Dennis M.J. Spectral effects on three dimensional reconstruction from x rays // Med. Physics. 1975. V.2. Is. 6. P. 321-324. DOI: 10.1118/1.594200.
26. Abella M, Martinez C, Desco M, Vaquero JJ, Fessler JA. Simplified Statistical Image Reconstruction for X-ray CT With Beam-Hardening Artifact Compensation. // IEEE Trans Med Imaging. 2020 Jan;39(1):111-118. DOI: 10.1109/TMI.2019.2921929.
27. Chukalina M. V., Ingacheva A., Buzmakov A. V., Polyakov I. V., Gladkov A., Yakimchuk I., Nikolaev D. P. 2017. Automatic beam hardening correction for CT reconstruction. // 31st European Conference on Modelling and Simulation (ECMS), Budapest, Hungary May 23-26, P. 270-275. DOI: 10.7148/2017-0270.
28. Rudin L.I., Osher S. and Fatemi E. Nonlinear total variation based noise removal algorithms. // Physica D. 1992. V.60. P/ 259-268. DOI: 10.1016/0167-2789(92)90242-F.
29. Kober V.I., Makoveckij A.YU., Voronin S.M., Karnauhov V.N. Bystryj algoritm regulyarizacii polnoj variacii dlya klassa radial'no-simmetrichnyh funkcij.[Fast algorithm of regularization of full variation for a class of radial-symmetric functions] // Informacionnye processy [Information processes] 2019. V. 19(1). P. 33–46.
30. Vlasov V. V., Konovalov A. B., Kolchugin S. V. Joint image reconstruction and segmentation: Comparison of two algorithms for few-view tomography // Computer Op-tics. 2019. V. 43(6) P. 1008-1020.DOI: 10/18287/2412-6179-2019-43-6-1008-1020.
31. Hejfec A.L. 3D modeli i algoritmy komp'yuternoj parametrizacii pri reshenii zadach konstruktivnoj geometrii (na nekotoryh istoricheskih primerah).[3D models and algorithms of computer parameterization in solving problems of structural geometry (on some historical examples)] // Vestnik YUUrGU. Seriya "Komp'yuternye tekhnologii, upravlenie, radioelektronika [Bulletin of SUSU. Computer Technologies, Control, Radio Electronics" series.] 2016. V. 16. № 2. С. 24-42. DOI: 10.14529/ctcr160203

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