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Impact of statistical reconstruction and compressed sensing algorithms on projection data elimination during X-ray CT image reconstruction

Oral Radiology volume 34, pages 237–244 (2018)Cite this article

Abstract

Objectives

To examine the effect of incomplete, or total elimination of, projection data on computed tomography (CT) images subjected to statistical reconstruction and/or compressed sensing algorithms.

Methods

Multidetector row CT images were used. The algebraic reconstruction technique (ART) and the maximum likelihood-expectation maximization (ML-EM) method were compared with filtered back-projection (FBP). Effects on reconstructed images were studied when the projection data of 360° (360 projections) were decreased to 180 or 90 projections by reducing the collection angle or thinning the image data. The total variation (TV) regularization method using compressed sensing was applied to images processed by the ART. Image noise was subjectively evaluated using the root-mean-square error and signal-to-noise ratio.

Results

When projection data were reduced by one-half or three-quarters, ART and ML-EM produced better image quality than FBP. Both ART and ML-EM resulted in high quality at a spread of 90 projections over 180° rotation. Computational loading was high for statistical reconstruction, but not for ML-EM, compared with the ART. TV regularization made it possible to use only 36 projections while still achieving acceptable image quality.

Conclusions

Incomplete projection data—accomplished by reducing the angle to collect image data or thinning the projection data without reducing the angle of rotation over which it is collected—made it possible to reduce the radiation dose while retaining image quality with statistical reconstruction algorithms and/or compressed sensing. Despite heavier computational calculation loading, these methods should be considered for reducing radiation doses.

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Acknowledgements

This study was supported by Grants-in-Aid for Scientific Research, #15K11065, Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan, 2015–2017.

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Authors and Affiliations

  1. Course of Medical Engineering, Graduate School of Engineering, Kitami Institute of Technology, 165 Koencho, Kitami, Hokkaido, 090-8507, Japan

    Bing-Yu Sun

  2. Department of Engineering on Intelligent Machines and Biomechanics, School of Regional Innovation and Social Design Engineering, Faculty of Engineering, Kitami Institute of Technology, 165 Koencho, Kitami, Hokkaido, 090-8507, Japan

    Yoshihiko Hayakawa

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Correspondence to Yoshihiko Hayakawa.

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Conflict of interest

Bing-Yu Sun and Yoshihiko Hayakawa declare that they have no conflict of interest.

Human rights statement

This study was approved by our institutional Review Board (IRB), which gave endorsement to human rights statements and informed consent in the study (IRB Approval No. 1002, Kitami Institute of Technology).

Informed consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Informed consent was obtained from all patients to be included in the study. Additional informed consent was obtained from all patients for whom identifying information is included in this article.

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This article does not contain any studies with animal subjects performed by any of the authors.

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Sun, BY., Hayakawa, Y. Impact of statistical reconstruction and compressed sensing algorithms on projection data elimination during X-ray CT image reconstruction. Oral Radiol 34, 237–244 (2018). https://doi.org/10.1007/s11282-017-0308-6

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  • DOI: https://doi.org/10.1007/s11282-017-0308-6

Keywords

  • Computed tomography 
  • X-ray
  • Image reconstruction
  • Statistical reconstruction
  • Incomplete projection data
  • Compressed sensing