Advertisement

Medical & Biological Engineering & Computing

, Volume 57, Issue 2, pp 489–503 | Cite as

A new software scheme for scatter correction based on a simple radiographic scattering model

  • K. Kim
  • S. Kang
  • W. Kim
  • C. Park
  • D. Lee
  • H. ChoEmail author
  • W. Kang
  • S. Park
  • G. Kim
  • H. Lim
  • H. Lee
  • J. Park
  • D. Jeon
  • Y. Lim
  • T. Woo
  • J. Oh
Original Article
  • 75 Downloads

Abstract

In common radiography, image contrast is often limited due mainly to scattered x-rays and noise, decreasing the quantitative usefulness of x-ray images. Several scatter reduction methods based on software correction schemes have been extensively investigated in an attempt to overcome these difficulties, most of which are based on measurement, mathematical-physical modeling, or a combination of both. However, those methods require special equipment, system geometry, and extra manual work to measure scatter characteristics. In this study, we investigated a new software scheme for scatter correction based on a simple radiographic scattering model where the intensity of the scattered x-rays was directly estimated from a single x-ray image using a weighted l1-norm contextual regularization framework. We implemented the proposed algorithm and performed a systematic simulation and experiment to demonstrate its viability. We also conducted some clinical image studies using patient’s image data of breast and L-spine to verify the clinical effectiveness of the proposed scheme. Our results indicate that the degradation of image characteristics by scattered x-rays and noise was effectively recovered by using the proposed software scheme, thus improving radiographic visibility considerably.

Graphical abstract

The schematic illustrations of scatter suppression methods by using a an antiscatter grid and b a scatter estimation algorithm.

Keywords

Scatter correction Software scheme Radiographic scattering model Single x-ray image 

Notes

Funding information

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-2017 R1A2B2002891).

References

  1. 1.
    Neitzel U (1992) Grids or air gaps for scatter reduction in digital radiography: a model calculation. Med Phys 19(2):475–481CrossRefGoogle Scholar
  2. 2.
    Siewerdsen JH, Moseley DJ, Bankhtiar B, Richard S, Jaffray DA (2004) The influence of antiscatter grids on soft-tissue detectability in cone-beam computed tomography with flat-panel detectors. Med Phys 31(12):3506–3520CrossRefGoogle Scholar
  3. 3.
    Seibert JA, Boone JM (1989) X-ray scatter removal by deconvolution. Med Phys 15(4):567–575CrossRefGoogle Scholar
  4. 4.
    Rana R, Setlur S, Bednarek D, Rudin S (2017) Real time implementation of anti-scatter grid elimination method for high resolution x-ray imaging CMOS detectors using graphics processing units (GPUs). Proc SPIE 10132, Medinal Imaging 2017: Physics of Medical Imaging, 101325Q (9 March 2017)Google Scholar
  5. 5.
    Siewerdsen J, Daly M, Bakhtiar B, Moseley D, Richard S, Keller H, Jaffray D (2006) A simple, direct method for x-ray scatter estimation and correction in digital radiography and cone-beam CT. Med Phys 33(1):187–197CrossRefGoogle Scholar
  6. 6.
    Gao H, Fahrig R, Bennett N, Sun M, Star-Lack J, Zhu L (2010) Scatter correction method for x-ray CT using primary modulation: phantom studies. Med Phys 37(2):934–946CrossRefGoogle Scholar
  7. 7.
    Diaz O (2013) Scattered radiation in projection X-ray mammography and digital breast tomosynthesis. PhD Thesis, University of SurreyGoogle Scholar
  8. 8.
    Binst J, Sterckx B, Bemelmans F, Cockmartin L, Van Peteghem N, Marshall N, Bosmans H (2015) Evaluation of automated CDMAM readings for non-standard CDMAM imaging conditions: grid-less acquisitions and scatter correction. Oxford University Press-Radiation Protection Dosimetry 165:1–4CrossRefGoogle Scholar
  9. 9.
    Ducote JL, Molloi S (2010) Scatter correction in digital mammography based on image deconvolution. Phys Med Biol 55(5):1295–1309CrossRefGoogle Scholar
  10. 10.
    Ahn SK, Cho G, Jeon H (2006) A scatter correction using thickness iteration in dual-energy radiography. IEEE Trans Nucl Sci 53(1):133–138CrossRefGoogle Scholar
  11. 11.
    Wang A, Shapiro E, Yoon S, Ganguly A, Proano C, Colbeth R, Lehto E, Star-Lack J (2015) Asymmetric scatter kernels for software-based scatter correction of gridless mammography. SPIE Medical Imaging 9412:1I-1-7Google Scholar
  12. 12.
    Rührnschopf EP, Klingenbeck K (2011) A general framework and review of scatter correction methods in x-ray cone-beam computerized tomography. Part 1: scatter compensation approaches. Med Phys 38(7):4296–4311CrossRefGoogle Scholar
  13. 13.
    Rührnschopf EP, Klingenbeck K (2011) A general framework and review of scatter correction methods in cone beam CT. Part 2: scatter estimation approaches. Med Phys 38(9):5186–5199CrossRefGoogle Scholar
  14. 14.
    Meng B, Lee H, Xing L, Fahimian BP (2013) Single-scan patient-specific scatter correction in computed tomography using peripheral detection of scatter and compressed sensing scatter retrieval. Med Phys 40(1):011907CrossRefGoogle Scholar
  15. 15.
    Tarel J, Hautière N (2009) Fast visibility restoration from a single color or gray level image. Proc of IEEE 12th International Conference on Computer Vision, Kyoto, Japan, Sep., 2201–2208Google Scholar
  16. 16.
    Meng G, Wang Y, Duan J, Xiang S, Pan C (2013) Efficient image dehazing with boundary constraint and contextual regularization. In Proc. IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, Dec.Google Scholar
  17. 17.
    Boyd S, Parikh N, Chu E, Peleato B, Eckstein J (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations Trends Machine Learning 3(1):1–122CrossRefGoogle Scholar
  18. 18.
    Hsu C, Palmeri N, Segars W, Veress A, Dobbins J III (2013) Generation of a suite of 3D computer-generated breast phantoms from a limited set of human subject data. Med Phys 40(4):043703 1–11CrossRefGoogle Scholar
  19. 19.
    Boone JM, Seibert JA (1988) An analytical model of the scattered radiation distribution in diagnostic radiology. Med Phys 15(5):721–725CrossRefGoogle Scholar
  20. 20.
    Boone J, Cooper III V (2000) Scatter/primary in mammography: Monte Carlo validation. Med Phys 27(8):1818–1831CrossRefGoogle Scholar
  21. 21.
    Leon S, Brateman L, Wagner L (2014) Characterization of scatter in digital mammography from use of Monte Carlo simulations and comparison to physical measurements. Med Phys 41(11):111914 1-14CrossRefGoogle Scholar
  22. 22.
    Gauntt M, Barnes G (2006) Grid line artifact formation: a comprehensive theory. Med Phys 33(6):1668–1677CrossRefGoogle Scholar
  23. 23.
    Kim D, Lee S (2013) Grid artifact reduction for direct digital radiography detectors based on rotated stationary grids with homomorphic filtering. Med Phys 40(6):061905 1–14CrossRefGoogle Scholar
  24. 24.
    Li Y, Cao H (2011) Virtual grid imaging method and system for eliminating scattered radiation effect, US patents, US8064676B2, Nov 22Google Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  • K. Kim
    • 1
  • S. Kang
    • 1
  • W. Kim
    • 1
  • C. Park
    • 1
  • D. Lee
    • 1
  • H. Cho
    • 1
    Email author
  • W. Kang
    • 1
  • S. Park
    • 1
  • G. Kim
    • 1
  • H. Lim
    • 1
  • H. Lee
    • 1
  • J. Park
    • 1
  • D. Jeon
    • 1
  • Y. Lim
    • 1
  • T. Woo
    • 1
  • J. Oh
    • 2
  1. 1.Department of Radiation Convergence EngineeringYonsei UniversityWonjuSouth Korea
  2. 2.Division of Convergence TechnologyNational Cancer CenterGoyangSouth Korea

Personalised recommendations