Advertisement

X-ray detection based on complementary metal-oxide-semiconductor sensors

  • Qian-Qian Cheng
  • Chun-Wang MaEmail author
  • Yan-Zhong Yuan
  • Fang Wang
  • Fu Jin
  • Xian-Feng Liu
Article
  • 53 Downloads

Abstract

Complementary metal-oxide-semiconductor (CMOS) sensors can convert X-rays into detectable signals; therefore, they are powerful tools in X-ray detection applications. Herein, we explore the physics behind X-ray detection performed using CMOS sensors. X-ray measurements were obtained using a simulated positioner based on a CMOS sensor, while the X-ray energy was modified by changing the voltage, current, and radiation time. A monitoring control unit collected video data of the detected X-rays. The video images were framed and filtered to detect the effective pixel points (radiation spots). The histograms of the images prove there is a linear relationship between the pixel points and X-ray energy. The relationships between the image pixel points, voltage, and current were quantified, and the resultant correlations were observed to obey some physical laws.

Keywords

X-ray detection Simulated positioner Complementary metal-oxide-semiconductor sensor Effective pixel points 

Notes

Acknowledgements

We thank Dr. Gong-Tao Fan and Prof. Hong-Wei Wang of Shanghai Institute of Applied Physics, CAS, for their kind help in the experiments.

References

  1. 1.
    M.J. Yaffe, J.A. Rowlands, X-ray detectors for digital radiography. Phys. Med. Biol. 42, 1 (1997).  https://doi.org/10.1088/0031-9155/42/1/001 CrossRefGoogle Scholar
  2. 2.
    X. Wang, S.L. Zhang, G.X. Song et al., Remote measurement of low-energy radiation based on ARM board and ZigBee wireless communication. Nucl. Sci. Tech. 29, 4 (2018).  https://doi.org/10.1007/s41365-017-0344-2 CrossRefGoogle Scholar
  3. 3.
    X. Wang, P. Knapp, S. Vaynman et al., Experimental study and analytical model of deformation of magnetostrictive films as applied to mirrors for X-ray space telescopes. Appl. Opt. 53(27), 6256–6267 (2014).  https://doi.org/10.1364/AO.53.006256 CrossRefGoogle Scholar
  4. 4.
    Z. Prieskorn, C.V. Griffith, S.D. Bongiorno et al., Characterization of Si hybrid CMOS detectors for use in the soft X-ray band. Nucl. Instrum. Methods Phys. A 717, 83–93 (2013).  https://doi.org/10.1016/j.nima.2013.03.057 CrossRefGoogle Scholar
  5. 5.
    C. Zhao, N. Vassiljev, A.C. Konstantinidis et al., Three-dimensional cascaded system analysis of a 50 µm pixel pitch wafer-scale CMOS active pixel sensor X-ray detector for digital breast tomosynthesis. Phys. Med. Biol. 62, 1994 (2017).  https://doi.org/10.1088/1361-6560/aa586c CrossRefGoogle Scholar
  6. 6.
    T. Patel, H. Peppard, M.B. Williams, Effects on image quality of a 2D antiscatter grid in X-ray digital breast tomosynthesis: initial experience using the dual modality (X-ray and molecular) breast tomosynthesis scanner. Med. Phys. 43, 1720–1735 (2016).  https://doi.org/10.1118/1.4943632 CrossRefGoogle Scholar
  7. 7.
    D. Magalotti, P. Placidi, M. Dionigi et al., Experimental characterization of a personal wireless sensor network for the medical X-ray dosimetry. IEEE Trans. Instrum. Meas. 65, 2002–2011 (2016).  https://doi.org/10.1109/TIM.2016.2534661 CrossRefGoogle Scholar
  8. 8.
    D.W. Lane, X-ray imaging and spectroscopy using low cost COTS CMOS sensors. Nucl. Instrum. Methods Phys. B 284, 29–32 (2012).  https://doi.org/10.1016/j.nimb.2011.09.007 CrossRefGoogle Scholar
  9. 9.
    S.L. Zhang, Q.Q. Cheng, D.F. Guo et al., Design of the new remote measurement system for low-energy radiation. Nucl. Electron. Detect. Technol. 37, 262–267 (2017).  https://doi.org/10.3969/j.issn.0258-0934.2017.03.008 CrossRefGoogle Scholar
  10. 10.
    Q.Q. Cheng, Y.Y. Zhong, C.W. Ma et al., Gamma measurement based on CMOS sensor and ARM microcontroller. Nucl. Sci. Tech. 28, 122 (2017).  https://doi.org/10.1007/s41365-017-0276-x CrossRefGoogle Scholar
  11. 11.
    D. Magalotti, L. Bissi, E. Conti et al., Performance of CMOS imager as sensing element for a real-time active pixel dosimeter for interventional radiology procedures. J. Instrum. 9, C01036 (2014).  https://doi.org/10.1088/1748-0221/9/01/C01036 CrossRefGoogle Scholar
  12. 12.
    E. Conti, P. Placidi, M. Biasini et al., Use of a CMOS image sensor for an active personal dosimeter in interventional radiology. IEEE Trans. Instrum. Meas. 62, 1065–1072 (2013).  https://doi.org/10.1109/TIM.2012.2223331 CrossRefGoogle Scholar
  13. 13.
    M. Pérez, J. Lipovetzky, M.S. Haro et al., Particle detection and classification using commercial off the shelf CMOS image sensors. Nucl. Instrum. Methods Phys. A 827, 171–180 (2016).  https://doi.org/10.1016/j.nima.2016.04.072 CrossRefGoogle Scholar
  14. 14.
    F. Wang, M.Y. Wang, Y.F. Liu et al., Obtaining low energy γ dose with CMOS sensors. Nucl. Sci. Tech. 25, 060401 (2014).  https://doi.org/10.13538/j.1001-8042/nst.25.060401 CrossRefGoogle Scholar
  15. 15.
    T. Ishiwatari, G. Beer, A.M. Bragadireanu et al., New analysis method for CCD X-ray data. Nucl. Instrum. Methods Phys. A 556, 509–515 (2006).  https://doi.org/10.1016/j.nima.2005.10.105 CrossRefGoogle Scholar
  16. 16.
    Q.Y. Wei, R. Bai, Z.P. Wang et al., Surveying ionizing radiations in real time using a smartphone. Nucl. Sci. Tech. 28, 70 (2017).  https://doi.org/10.1007/s41365-017-0215-x CrossRefGoogle Scholar
  17. 17.
    F. Wang, M.Y. Wang, F.S. Tian et al., Study on two-dimensional distribution of X-ray image based on improved Elman algorithm. Radiat. Meas. 77, 1–4 (2015).  https://doi.org/10.1016/j.radmeas.2015.03.012 CrossRefGoogle Scholar
  18. 18.
    A.C. Konstantinidis, M.B. Szafraniec, R.D. Speller et al., The Dexela 2923 CMOS X-ray detector: a flat panel detector based on CMOS active pixel sensors for medical imaging applications. Nucl. Instrum. Methods Phys. A 689, 12–21 (2012).  https://doi.org/10.1016/j.nima.2012.06.024 CrossRefGoogle Scholar
  19. 19.
    H.G. Kang, J.J. Song, K. Lee et al., An investigation of medical radiation detection using CMOS image sensors in smartphones. Nucl. Instrum. Methods Phys. A 823, 126–134 (2016).  https://doi.org/10.1016/j.nima.2016.04.007 CrossRefGoogle Scholar
  20. 20.
    W. Kang, H. Li, F. Deng, Direct gray-scale extraction of topographic features for vein recognition. Sci. China Inf. Sci. 53, 2062–2074 (2010).  https://doi.org/10.1007/s11432-010-4064-z CrossRefGoogle Scholar
  21. 21.
    J.C. Russ, The Image Processing Handbook (CRC Press, Boca Raton, 2016)zbMATHGoogle Scholar

Copyright information

© China Science Publishing & Media Ltd. (Science Press), Shanghai Institute of Applied Physics, the Chinese Academy of Sciences, Chinese Nuclear Society and Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Qian-Qian Cheng
    • 1
  • Chun-Wang Ma
    • 1
    Email author
  • Yan-Zhong Yuan
    • 2
  • Fang Wang
    • 2
  • Fu Jin
    • 3
  • Xian-Feng Liu
    • 3
  1. 1.College of Physics and Materials ScienceHenan Normal UniversityXinxiangChina
  2. 2.College of Electronic and Electrical EngineeringHenan Normal UniversityXinxiangChina
  3. 3.Chongqing Cancer HospitalChongqingChina

Personalised recommendations