Advertisement

Comparison of Analytical BP-FBP and Algebraic SART-SIRT Image Reconstruction Methods in Computed Tomography for the Oil Measurement System

  • Lotfi ZarourEmail author
  • Galina F. Malykhina
Conference paper
  • 190 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 95)

Abstract

An imbalance between the produced oil entering the pipeline and the oil received by consumers is a real problem. To solve the problem, we propose to use methods of computed tomography. The article is devoted to investigating methods for reconstructing a section of a pipeline to determine time intervals over which no gas inclusions in the oil flow occur. Computed tomography is based on image reconstruction methods. We made comparison between analytical reconstruction techniques: Back Projection (BP) and Filtered Back Projection (FBP) and iterative reconstruction techniques: Simultaneous Algebraic Reconstruction Technique (SART) and Simultaneous Iterative Reconstruction Technique (SIRT); the simulation was performed using Astratoolbox, an open source image reconstruction tool for tomography, and then the reconstructed images were compared using the relative root mean square error and a conclusion was achieved. The results demonstrate that the SIRT and SART method have given the closest to each other reconstructed images.

Keywords

Oil imbalance Industry computed tomography Simultaneous algebraic reconstruction Simultaneous iterative reconstruction 

References

  1. 1.
    Andersen, A.: Simultaneous algebraic reconstruction technique (SART): a superior implementation of the ART algorithm. Ultrason. Imaging 6(1), 81–94 (1984)CrossRefGoogle Scholar
  2. 2.
    Boudjelal, A., Messali, Z., Elmoataz, A., Attallah, B.: Improved simultaneous algebraic reconstruction technique algorithm for positron-emission tomography image reconstruction via minimizing the fast total variation. J. Med. Imaging Radiat. Sci. 48(4), 385–393 (2017)CrossRefGoogle Scholar
  3. 3.
    Aarle, W.V., Palenstijn, W.J., Beenhouwer, J.D., Altantzis, T., Bals, S., Batenburg, K.J., Sijbers, J.: The ASTRA toolbox: a platform for advanced algorithm development in electron tomography. Ultramicroscopy 157, 35–47 (2015)CrossRefGoogle Scholar
  4. 4.
    Aarle, W.V., Palenstijn, W.J., Cant, J., Janssens, E., Bleichrodt, F., Dabravolski, A., Beenhouwer, J.D., Batenburg, K.J., Sijbers, J.: Fast and flexible X-ray tomography using the ASTRA toolbox. Opt. Express 24(22), 25129–25147 (2016).  https://doi.org/10.1364/OE.24.025129ADSCrossRefGoogle Scholar
  5. 5.
    Kalaga, D.V., Kulkarni, A.V., Acharya, R., Kumar, U., Singh, G., Joshi, J.B.: Some industrial applications of gamma-ray tomography. J. Taiwan Inst. Chem. Eng. 40(6), 602–612 (2009)CrossRefGoogle Scholar
  6. 6.
    Abdullah, J., Cassanello, M.C.F., Dudukovic, M.P., Dyakowski, T., Hamada, M.M., Jin, J.H.: Industrial Process Gamma Tomography, IAEA, Vienna, Austria (2008)Google Scholar
  7. 7.
    Banjak, H., Grenier, T., Epicier, T., Koneti, S., Roiban, L., Gay, A.-S., Magnin, I., Peyrin, F., Maxim, V.: Evaluation of noise and blur effects with SIRT-FISTA-TV reconstruction algorithm: application to fast environmental transmission electron tomography. Ultramicroscopy 189, 109–123 (2018)CrossRefGoogle Scholar
  8. 8.
    Chetih, N., Messali, Z.: Tomographic image reconstruction using filtered back projection (FBP) and algebraic reconstruction technique (ART). In: Proceedings of the 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT) (2015)Google Scholar
  9. 9.
    Rit, S., Sarrut, D., Desbat, L.: Comparison of analytic and algebraic methods for motion-compensated cone-beam CT reconstruction of the thorax. Proc. IEEE Trans. Med. Imaging 28(10), 1513–1525 (2009)CrossRefGoogle Scholar
  10. 10.
    Vijayalakshmi, G., Vindhya, P.: Comparison of algebraic reconstruction methods in computed tomography. Int. J. Comput. Sci. Inf. Technol. 5, 6007–6009 (2014)Google Scholar
  11. 11.
    Aarle, W.V., Batenburg, K.J., Gompel, G.V., Casteele, E.V.D., Sijbers, J.: Super-resolution for computed tomography based on discrete tomography. IEEE Trans. Image Process. 23(3), 1181–1193 (2014)ADSMathSciNetCrossRefGoogle Scholar
  12. 12.
    Johansen, G.: Gamma-ray tomography. Ind. Tomogr. 197–222 (2015)Google Scholar
  13. 13.
    Askari, M., Taheri, A., Larijani, M.M., Movafeghi, A.: Industrial gamma computed tomography using high aspect ratio scintillator detectors (A Geant4 simulation). Nucl. Instrum. Methods Phys. Res. Sect. A 923, 109–117 (2019)ADSCrossRefGoogle Scholar
  14. 14.
    Mesquita, C.H.D., Carvalho, D.V.D.S., Kirita, R., Vasquez, P.A.S., Hamada, M.M.: Gas–liquid distribution in a bubble column using industrial gamma-ray computed tomography. Radiat. Phys. Chem. 95, 396–400 (2014)ADSCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Peter the Great St. Petersburg Polytechnic UniversitySt. PetersburgRussia
  2. 2.Russian State Scientific Center for Robotics and Technical CyberneticsSt. PetersburgRussia

Personalised recommendations