Advertisement

Image Quality Optimization and Soft Tissue Visualization in Cone-Beam CT Imaging

  • Aude Castonguay-Henri
  • Dmitri Matenine
  • Matthieu Schmittbuhl
  • Jacques A. de Guise
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 68/1)

Abstract

Cone Beam CT is a well-established diagnostic tool for numerous applications. While providing better spatial resolution and exposing the patient to lower radiation doses than conventional CT, it is also subject to spatially dependent bias due to the beam energy spectrum, resulting in a very limited capacity for soft-tissue and quantitative imaging. The goal of this work is to improve image contrast resolution and density quantification, to reinforce diagnosis efficiency and accuracy. An iterative polyenergetic approach is adapted to CBCT in order to reduce the artifacts caused by the beam hardening phenomenon and monoenergetic approximations at reconstruction level. It integrates the X-ray spectrum of the source and the cone-beam geometry, and is based on the Iterative Maximum-likelihood Algorithm for CT (IMPACT), which defines the energy-dependent attenuation coefficient as a linear combination of photoelectric and Compton effects. Our preliminary results demonstrate reduction of cupping and successful quantitative reconstruction of simple phantoms using simulated and experimental CBCT data.

Keywords

Cone beam CT Iterative reconstruction Spectral reconstruction Soft tissue imaging 

Notes

Acknowledgements

This work was partly financially supported by Mitacs through the Mitacs Accelerate program and by Canada Research Chairs. The authors would like to thank Useful Progress Service Inc. (Montreal, QC) and its founder Francis Siguenza for financially supporting the Mitacs Program.

Conflicts of Interest

Dmitri Matenine is an employee of Useful Progress Services Inc. (Montreal, QC). Other authors declare that they have no conflict of interest.

References

  1. 1.
    Feldkamp, L.A., Davis, L.C., Kress, J.W. (1984). Practical cone-beam algorithm. J. Opt. Soc. Am. A, 1(6). doi: https://doi.org/10.1364/josaa.1.000612.
  2. 2.
    Nuyts, J., De Man, B., Fessler, J.A., Zbijewski, W., Beekman, F.J. (2013). Modelling the physics in the iterative reconstruction for transmission computed tomography. Physics in Medicine and Biology, 58(12). doi: https://doi.org/10.1088/0031-9155/58/12/r63.
  3. 3.
    Alvarez, R.E., Macovski, A. (1976). Energy-selective reconstructions in X-ray computerized tomography. Physics in Medicine and Biology, 21(5). doi: https://doi.org/10.1088/0031-9155/21/5/002.
  4. 4.
    De Man, B. et al. (2001). An Iterative Maximum-Likelihood Polychromatic Algorithm for CT. IEEE Transactions on Medical Imaging, 20(10), 999-1008. doi: https://doi.org/10.1109/42.959297.
  5. 5.
    Beister, M., Kolditz, D., Kalender, W.A. (2012). Iterative reconstruction methods in X-ray CT. Physica Medica, 28(2). doi: https://doi.org/10.1016/j.ejmp.2012.01.003.
  6. 6.
    Bushberg, J.T., Seibert, J.A., Leidholdt, E.M., Boone, J.M. (2012). The Essential Physics of Medical Imaging, 3rd edition. Lippincott Williams & Wilkins, Philadephia, USA. isbn: 978-0-7817-8057-5.Google Scholar
  7. 7.
    Siddon, R. (1985). Fast calcuation of the exact radiological path for 3-D CT. Medical Physics, 12(2). doi: https://doi.org/10.1118/1.595715
  8. 8.
    J. Nuyts, B. De Man, P. Dupont, M. Defrise, P. Suetens, and L. Mortelmans. (1998). Iterative reconstruction for helical CT: A simulation study. Phys. Med. Biol., vol. 43, pp. 729737.Google Scholar
  9. 9.
    Matenine, D., Mascolo-Fortin, J., Goussard, Y. & Després, P. (2015). Evaluation of the OSCTV iterative reconstruction algorithm for cone-beam optical CT. Medical Physics, 42(6376). doi: https://doi.org/10.1118/1.4931604.
  10. 10.
    S. Rit, M.V. Oliva, S. Brousmiche, R. Labarbe, D. Sarrut, and G.C. Sharp. (2014). The Reconstruction Toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the Insight Toolkit (ITK). Journal of Physics: Conference Series, 489(1).Google Scholar
  11. 11.
    Berger, M.J., Hubbell, J.H., Seltzer, S.M., Chang, J., Coursey, J.S., Sukumar, R., Zucker, D.S., and Olsen, K. (2010), XCOM: Photon Cross Section Database (version 1.5). [Online] Available: http://physics.nist.gov/xcom [2018, January 25]. National Institute of Standards and Technology, Gaithersburg, MD.
  12. 12.
    K. Carney, B. J. Gilmore, G. W. A. Fogarty and L. Desponds. (1997). Catalogue of Diagnostic X-ray Spectra and Other Data: Report No 78, Institute of Physics and Engineering in Medicine.Google Scholar
  13. 13.
    Hsieh, J. (2009). Computed Tomography: Principles, Design, Artifacts, and Recent Advances (2e dition). SPIE Press.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Aude Castonguay-Henri
    • 1
    • 2
  • Dmitri Matenine
    • 1
    • 2
  • Matthieu Schmittbuhl
    • 1
    • 3
  • Jacques A. de Guise
    • 1
    • 2
  1. 1.Laboratoire de Recherche en Imagerie et OrthopédieCentre de Recherche du Centre Hospitalier de L’Université de MontréalMontréalCanada
  2. 2.Département de Génie de La Production AutomatiséeÉcole de Technologie SupérieureMontréalCanada
  3. 3.Faculté de Médecine DentaireUniversité de MontréalMontréalCanada

Personalised recommendations