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)


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.


Cone beam CT Iterative reconstruction Spectral reconstruction Soft tissue imaging 



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.


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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

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