Journal of the Korean Physical Society

, Volume 76, Issue 1, pp 66–72 | Cite as

Dynamically-Collimated Digital Tomosynthesis Reconstruction by Using a Compressed-Sensing Based Algorithm

  • Soyoung Park
  • Guna Kim
  • Hyosung ChoEmail author
  • Changwoo Seo
  • Minsik Lee


Conventional digital tomosynthesis (DTS) reconstruction by using the filtered-backprojection algorithm requires a full field-of-view scan and relatively dense projections to obtain high-quality images, which results in a high radiation dose to patients. Interior DTS (iDTS) with a proper collimator offers a possible imaging modality for reducing the dose of radiation delivered because the X-ray beam is able to target a small region-of-interest (ROI) containing the target area. Collimators for iDTS often have a fixed rectangular shape, but focusing the X-ray beam on an arbitrarily shaped ROI would be preferable because it further reduces the excessive radiation dose. In this study, we propose a new iDTS scan method to create an ROI with an arbitrary shape to minimize the radiation dose at each angle of view. We used a compressed-sensing-based algorithm for accurate iDTS reconstruction. To validate the proposed method, we performed a systematic simulation and an experiment, and we investigated the image characteristics. Our results indicate that the proposed method may effectively reduce radiation dose in iDTS in real imaging systems.


Interior digital tomosynthesis Dynamic collimation Compressed-sensing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korea Ministry of Science and ICT (NRF-2017R1A2B2002891).


  1. [1]
    D. Godfrey, H. McAdams and J. Dobbins III, Med. Phys. 33, 655 (2006).CrossRefGoogle Scholar
  2. [2]
    I. Sechopoulos and C. Ghetti, Med. Phys. 36, 1199 (2009).CrossRefGoogle Scholar
  3. [3]
    Y. Zou, X. Pan and E. Sidky, Phys. Med. Biol. 50, 13 (2005).CrossRefGoogle Scholar
  4. [4]
    H. Kudo, T. Suzuki1 and E. Rashed, Quant. Imaging Med. Surg. 3, 147 (2013).Google Scholar
  5. [5]
    H. Gong et al., Med. Phys. 44, 71 (2017).CrossRefGoogle Scholar
  6. [6]
    F. Hashimoto, A. Teramoto, Y. Asada and S. Suzuki, Radiol. Phys. Technol. 10, 60 (2017).CrossRefGoogle Scholar
  7. [7]
    G. Wang, H. Yu and B. De Man, Med. Phys. 35, 1051 (2008).CrossRefGoogle Scholar
  8. [8]
    K. Choi et al., Med. Phys. 37, 5113 (2010).CrossRefGoogle Scholar
  9. [9]
    H. Yu and G. Wang, Phys. Med. 54, 2791 (2010).Google Scholar
  10. [10]
    Y. Park et al., Nucl. Instr. Meth. 804, 72 (2015).ADSCrossRefGoogle Scholar
  11. [11]
    R. Krame et al., Phys. Med. Biol. 55, 163 (2010).CrossRefGoogle Scholar
  12. [12]
    S. Jin and O. Kwon, J. Biom. Eng. Res. 35, 132 (2014).CrossRefGoogle Scholar

Copyright information

© The Korean Physical Society 2020

Authors and Affiliations

  • Soyoung Park
    • 1
  • Guna Kim
    • 1
  • Hyosung Cho
    • 1
    Email author
  • Changwoo Seo
    • 1
  • Minsik Lee
    • 2
  1. 1.Department of Radiation Convergence EngineeringYonsei UniversityWonjuKorea
  2. 2.Department of Radiation OncologyAsan Medical CenterSeoulKorea

Personalised recommendations