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Computed tomography imaging with the Adaptive Statistical Iterative Reconstruction (ASIR) algorithm: dependence of image quality on the blending level of reconstruction

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Abstract

Computed tomography (CT) is a useful and widely employed imaging technique, which represents the largest source of population exposure to ionizing radiation in industrialized countries. Adaptive Statistical Iterative Reconstruction (ASIR) is an iterative reconstruction algorithm with the potential to allow reduction of radiation exposure while preserving diagnostic information. The aim of this phantom study was to assess the performance of ASIR, in terms of a number of image quality indices, when different reconstruction blending levels are employed. CT images of the Catphan-504 phantom were reconstructed using conventional filtered back-projection (FBP) and ASIR with reconstruction blending levels of 20, 40, 60, 80, and 100%. Noise, noise power spectrum (NPS), contrast-to-noise ratio (CNR) and modulation transfer function (MTF) were estimated for different scanning parameters and contrast objects. Noise decreased and CNR increased non-linearly up to 50 and 100%, respectively, with increasing blending level of reconstruction. Also, ASIR has proven to modify the NPS curve shape. The MTF of ASIR reconstructed images depended on tube load/contrast and decreased with increasing blending level of reconstruction. In particular, for low radiation exposure and low contrast acquisitions, ASIR showed lower performance than FBP, in terms of spatial resolution for all blending levels of reconstruction. CT image quality varies substantially with the blending level of reconstruction. ASIR has the potential to reduce noise whilst maintaining diagnostic information in low radiation exposure CT imaging. Given the opposite variation of CNR and spatial resolution with the blending level of reconstruction, it is recommended to use an optimal value of this parameter for each specific clinical application.

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Acknowledgements

We would like to thank Prof. Duccio Volterrani, Dr. Antonio Traino and Dr. Davide Giustini for supporting this work. Also, the authors are grateful to the anonymous Reviewers for constructive and helpful comments to improve the manuscript.

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Authors and Affiliations

  1. Department of Physics, University of Pisa, Pisa, Italy

    Patrizio Barca & Maria Evelina Fantacci

  2. INFN, Pisa section, Pisa, Italy

    Patrizio Barca & Maria Evelina Fantacci

  3. Unit of Medical Physics, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Via Roma 67, 56126, Pisa, Italy

    Marco Giannelli

  4. Diagnostic and Interventional Radiology, University of Pisa, Pisa, Italy

    Davide Caramella

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  1. Patrizio Barca
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  2. Marco Giannelli
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Correspondence to Marco Giannelli.

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The authors declare that they have no conflict of interest.

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Given that our study is a phantom study, the ethical approval statement is not needed/applicable.

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Barca, P., Giannelli, M., Fantacci, M.E. et al. Computed tomography imaging with the Adaptive Statistical Iterative Reconstruction (ASIR) algorithm: dependence of image quality on the blending level of reconstruction. Australas Phys Eng Sci Med 41, 463–473 (2018). https://doi.org/10.1007/s13246-018-0645-8

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  • DOI: https://doi.org/10.1007/s13246-018-0645-8

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