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Applicability and potential of monitored reconstruction in computed tomography

Author

Listed:
  • Marat Gilmanov
  • Konstantin Bulatov
  • Oleg Bugai
  • Anastasia Ingacheva
  • Marina Chukalina
  • Dmitrii Nikolaev
  • Vladimir Arlazarov

Abstract

Monitored tomographic reconstruction (MTR) is a potentially powerful tool for dose and time reduction in computed tomography scanning. We are the first to study the issue of practical implementation of MTR protocols in current-generation real-life instruments. We propose an empirical quantitative model for calculating acquisition and reconstruction times. It is demonstrated that projection acquisition order has a significant impact on the time and dose of tomographic experiments. The new alternative acquisition most suitable for MTR protocols is proposed. To estimate the restrictions and scope of applicability for MTR four typical commercial setups are studied within a proposed model. We construct an experimental stand for achieving a real-time reconstruction, together with validation of the proposed acquisition time model. We demonstrate that real-time reconstruction may be implemented without slowing down an acquisition process. An optimization of reconstruction from partial data is proposed, which allowed the production of 385 and 440 reconstructions for standard and proposed acquisition orders correspondingly during a single acquisition of 512 projections. The results of the study demonstrate that with proposed optimizations MTR can be effectively utilized for practical applications using the current generation of existing setups in industrial and nano tomography fields.

Suggested Citation

  • Marat Gilmanov & Konstantin Bulatov & Oleg Bugai & Anastasia Ingacheva & Marina Chukalina & Dmitrii Nikolaev & Vladimir Arlazarov, 2024. "Applicability and potential of monitored reconstruction in computed tomography," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-20, July.
  • Handle: RePEc:plo:pone00:0307231
    DOI: 10.1371/journal.pone.0307231
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    References listed on IDEAS

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    1. Dmitry Polevoy & Marat Gilmanov & Danil Kazimirov & Marina Chukalina & Anastasia Ingacheva & Petr Kulagin & Dmitry Nikolaev, 2023. "Tomographic Reconstruction: General Approach to Fast Back-Projection Algorithms," Mathematics, MDPI, vol. 11(23), pages 1-37, November.
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