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System Matrix Analysis for Computed Tomography Imaging

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  • Liubov Flores
  • Vicent Vidal
  • Gumersindo Verdú

Abstract

In practical applications of computed tomography imaging (CT), it is often the case that the set of projection data is incomplete owing to the physical conditions of the data acquisition process. On the other hand, the high radiation dose imposed on patients is also undesired. These issues demand that high quality CT images can be reconstructed from limited projection data. For this reason, iterative methods of image reconstruction have become a topic of increased research interest. Several algorithms have been proposed for few-view CT. We consider that the accurate solution of the reconstruction problem also depends on the system matrix that simulates the scanning process. In this work, we analyze the application of the Siddon method to generate elements of the matrix and we present results based on real projection data.

Suggested Citation

  • Liubov Flores & Vicent Vidal & Gumersindo Verdú, 2015. "System Matrix Analysis for Computed Tomography Imaging," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-12, November.
    • Handle: RePEc:plo:pone00:0143202
    DOI: 10.1371/journal.pone.0143202
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    References listed on IDEAS

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    1. Wei Yu & Li Zeng, 2014. "A Novel Weighted Total Difference Based Image Reconstruction Algorithm for Few-View Computed Tomography," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-10, October.
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    Cited by:

    1. Ignacio Algredo-Badillo & José Julio Conde-Mones & Carlos Arturo Hernández-Gracidas & María Monserrat Morín-Castillo & José Jacobo Oliveros-Oliveros & Claudia Feregrino-Uribe, 2020. "An FPGA-based analysis of trade-offs in the presence of ill-conditioning and different precision levels in computations," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-26, June.
    2. Jian Fu & Zhenzhong Liu & Jingzheng Wang, 2016. "Multi-Mounted X-Ray Computed Tomography," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-15, April.

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