Iterative High Resolution Tomography from Combined High-Low Resolution Sinogram Pairs

  • László VargaEmail author
  • Rajmund Mokso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11255)


In some cases of tomography we can only gain high resolution projections of the object with only partial coverage, whereas only a small part of the object – a given Region of Interest (ROI) – is fully covered by high resolution projections. In such cases the structures outside the region of interest cause artefacts to appear in the reconstructed image and degrade the image quality of the tomogram. We proposed three new iterative approaches for the accurate reconstruction of the ROI by combining a high resolution set of projections, with low resolution full field of view projections and prior information. We also evaluate our methods reconstructing software phantoms, and compare their performance to other methods in the literature.


Tomography Reconstruction Region of interest ROI Sinogram combination GPGPU 



This research was supported by the project “Integrated program for training new generation of scientists in the fields of computer science”, no EFOP-3.6.3-VEKOP-16-2017-0002. The project has been supported by the European Union and co-funded by the European Social Fund. We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Tesla K40 GPU used for this research.


  1. 1.
    Batenburg, K.J., Sijbers, J.: Dart: a practical reconstruction algorithm for discrete tomography. IEEE Trans. Image Process. 20(9), 2542–2553 (2011)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Chen, L., et al.: Dual resolution cone beam breast CT: a feasibility study. Med. Phys. 36(9Part1), 4007–4014 (2009)CrossRefGoogle Scholar
  3. 3.
    Chityala, R., Hoffmann, K.R., Rudin, S., Bednarek, D.R.: Region of interest (ROI) computed tomography (CT): comparison with full field of view (FFOV) and truncated CT for a human head phantom. In: Flynn, M.J. (ed.) Medical Imaging 2005: Physics of Medical Imaging. SPIE, April 2005Google Scholar
  4. 4.
    Cho, P.S., Rudd, A.D., Johnson, R.H.: Cone-beam CT from width-truncated projections. Comput. Med. Imaging Graph. 20(1), 49–57 (1996)CrossRefGoogle Scholar
  5. 5.
    Chun, I.K., Cho, M.H., Lee, S.C., Cho, M.H., Lee, S.Y.: X-ray micro-tomography system for small-animal imaging with zoom-in imaging capability. Phys. Med. Biol. 49(17), 3889–3902 (2004)CrossRefGoogle Scholar
  6. 6.
    Courdurier, M., Noo, F., Defrise, M., Kudo, H.: Solving the interior problem of computed tomography using a priori knowledge. Inverse Prob. 24(6), 065001 (2008)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Gentle, D.J., Spyrou, N.M.: Region of interest tomography in industrial applications. Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip. 299(1), 534–537 (1990)CrossRefGoogle Scholar
  8. 8.
    Herman, G.T.: Fundamentals of Computerized Tomography: Image Reconstruction from Projections. Springer, Heidelberg (2009). Scholar
  9. 9.
    Huesman, R.H.: A new fast algorithm for the evaluation of regions of interest and statistical uncertainty in computed tomography. Phys. Med. Biol. 29(5), 543–552 (1984)CrossRefGoogle Scholar
  10. 10.
    Kadrmas, D.J., Jaszczak, R.J., McCormick, J.W., Coleman, R.E.: Truncation artifact reduction in transmission CT for improved SPECT attenuation compensation. Phys. Med. Biol. 40(6), 1085–1104 (1995)CrossRefGoogle Scholar
  11. 11.
    Kak, A.C., Slaney, M.: Principles of Computerized Tomographic Imaging. IEEE Press, New York (1999)zbMATHGoogle Scholar
  12. 12.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 (2014)
  13. 13.
    Kudo, H., Courdurier, M., Noo, F., Defrise, M.: Tiny a prioriknowledge solves the interior problem in computed tomography. Phys. Med. Biol. 53(9), 2207–2231 (2008)CrossRefGoogle Scholar
  14. 14.
    Kyrieleis, A., Titarenko, V., Ibison, M., Connolley, T., Withers, P.J.: Region-of-interest tomography using filtered backprojection: assessing the practical limits. J. Microsc. 241(1), 69–82 (2010)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Lauritsch, G., Bruder, H.: Technical report: head phantom. Technical report, Institute of Medical Physics, Friedrich-Alexander-University Erlangen-Nrnberg (2009)Google Scholar
  16. 16.
    Maaß, C., Knaup, M., Kachelrieß, M.: New approaches to region of interest computed tomography. Med. Phys. 38(6Part1), 2868–2878 (2011)CrossRefGoogle Scholar
  17. 17.
    Patel, V., Hoffmann, K.R., Ionita, C.N., Keleshis, C., Bednarek, D.R., Rudin, S.: Rotational micro-CT using a clinical C-arm angiography gantry. Med. Phys. 35(10), 4757–4764 (2008)CrossRefGoogle Scholar
  18. 18.
    Reimers, P., Kettschau, A., Goebbels, J.: Region-of-interest (ROI) mode in industrial X-ray computed tomography. NDT Int. 23(5), 255–261 (1990)CrossRefGoogle Scholar
  19. 19.
    Sourbelle, K., Kachelriess, M., Kalender, W.A.: Reconstruction from truncated projections in CT using adaptive detruncation. Eur. Radiol. 15(5), 1008–1014 (2005)CrossRefGoogle Scholar
  20. 20.
    van der Sluis, A., van der Vorst, H.A.: SIRT- and CG-type methods for the iterative solution of sparse linear least-squares problems. Linear Algebra Appl. 130, 257–303 (1990)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Weber, S., Nagy, A., Schüle, T., Schnörr, C., Kuba, A.: A benchmark evaluation of large-scale optimization approaches to binary tomography. In: Kuba, A., Nyúl, L.G., Palágyi, K. (eds.) DGCI 2006. LNCS, vol. 4245, pp. 146–156. Springer, Heidelberg (2006). Scholar
  22. 22.
    Yu, H., Wang, G.: Compressed sensing based interior tomography. Phys. Med. Biol. 54(9), 2791–2805 (2009)CrossRefGoogle Scholar
  23. 23.
    Yu, H., Yang, J., Jiang, M., Wang, G.: Supplemental analysis on compressed sensing based interior tomography. Phys. Med. Biol. 54(18), N425–N432 (2009)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Image Processing and Computer GraphicsUniversity of SzegedSzegedHungary
  2. 2.Max IV LaborarotyLund UniversityLundSweden

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