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Journal of the Korean Physical Society

, Volume 74, Issue 1, pp 57–62 | Cite as

Sparse-View Reconstruction in Dental Computed Tomography by Using a Dictionary-Learning Based Method

  • Guna Kim
  • Soyoung Park
  • Chulkyu Park
  • Dongyeon Lee
  • Younghwan Lim
  • Kyuseok Kim
  • Woosung Kim
  • Hyosung ChoEmail author
  • Changwoo Seo
  • Hyunwoo Lim
  • Hunwoo Lee
  • Seokyoon Kang
  • Jeongeun Park
  • Duhee Jeon
Article
  • 1 Downloads

Abstract

In this study, we investigated sparse-view reconstruction in dental computed tomography (DCT) by using a dictionary-learning (DL)-based method to reduce excessive radiation dose to patients. In sparse-view DCT, only a small number (< 100) of projections, far less than what is required by the Nyquist sampling theory, are acquired from the imaging system and used for image reconstruction. DL is a representation learning theory that aims to find a sparse representation of the input signal in the form of a linear combination of basic elements (or atoms). We implemented a DL-based reconstruction algorithm and performed a systematic simulation and an experiment to evaluate the algorithm’s effectiveness for sparse-view reconstruction in DCT. DCT images were reconstructed using the three sparse-view projections of P30, P40, and P60, and their image qualities were quantitatively evaluated in terms of the intensity profile, the universal quality index, and the peak signal-to-noise ratio. The hardware system used in the experiment consisted of an X-ray tube, which was run at 90 kVp and 40 mA, and a flat-panel detector with a 388-μm pixel size. Our simulation and experimental results indicate that the DL-based method significantly reduced streak artifacts in the sparse-view DCT reconstruction when using P40, thus maintaining image quality.

Keywords

Dental computed tomography Sparse-view Dictionary-learning Low radiation dosage 

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Copyright information

© The Korean Physical Society 2019

Authors and Affiliations

  • Guna Kim
    • 1
  • Soyoung Park
    • 1
  • Chulkyu Park
    • 1
  • Dongyeon Lee
    • 1
  • Younghwan Lim
    • 1
  • Kyuseok Kim
    • 1
  • Woosung Kim
    • 1
  • Hyosung Cho
    • 1
    Email author
  • Changwoo Seo
    • 1
  • Hyunwoo Lim
    • 1
  • Hunwoo Lee
    • 1
  • Seokyoon Kang
    • 1
  • Jeongeun Park
    • 1
  • Duhee Jeon
    • 1
  1. 1.Department of Radiation Convergence EngineeringYonsei UniversityWonjuKorea

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