ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

Geometric calibration of a computed laminography system for high-magnification nondestructive test imaging

  • Chae, Seung-Hoon (Medical Information Research Section, Electronics and Telecommunications Research Institute) ;
  • Son, Kihong (Medical Information Research Section, Electronics and Telecommunications Research Institute) ;
  • Lee, Sooyeul (Medical Information Research Section, Electronics and Telecommunications Research Institute)
  • Received : 2021.04.22
  • Accepted : 2022.07.22
  • Published : 2022.10.10
Download PDF
⟨ Previous Next ⟩

Abstract

Nondestructive testing, which can monitor a product's interior without disassembly, is becoming increasingly essential for industrial inspection. Computed laminography (CL) is widely used in this application, as it can reconstruct a product, such as a printed circuit board, into a three-dimensional (3D) high-magnification image using X-rays. However, such high-magnification scanning environments can be affected by minute vibrations of the CL device, which can generate motion artifacts in the 3D reconstructed image. Since such vibrations are irregular, geometric corrections must be performed at every scan. In this paper, we propose a geometry calibration method that can correct the geometric information of CL scans based on the image without using geometry calibration phantoms. The proposed method compares the projection and digitally reconstructed radiography images to measure the geometric error. To validate the proposed method, we used both numerical phantom images at various magnifications and images obtained from real industrial CL equipment. The experiment results confirmed that sharpness and contrast-to-noise ratio (CNR) were improved.

Keywords

Acknowledgement

The authors would like to thank SEC Co., Ltd. for providing the valuable CL scan images of the PCB. This work was supported by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: KMDF_PR_20200901_0016, 9991006689).

References

  1. N. S. O'Brien, R. P. Boardman, I. Sinclair, and T. Blumensath, Recent advances in X-ray cone-beam computed laminography, J. Xray Sci. Technol. 24 (2016), no. 5, 691-707.
  2. P. Aryan, S. Sampath, and H. Sohn, An overview of nondestructive testing methods for integrated circuit packaging inspection, Sensors 18 (2018), no. 7, 1981. https://doi.org/10.3390/s18071981
  3. S. L. Fisher, D. J. Holmes, J. S. Jorgensen, P. Gajjar, J. Behnsen, W. R. B. Lionheart, and P. J. Withers, Laminography in the lab: Imaging planar objects using a conventional x-ray CT scanner, Meas. Sci. Technol. 30 (2019), no. 3, 35401. https://doi.org/10.1088/1361-6501/aafcae
  4. L. De Chiffre, S. Carmignato, J. P. Kruth, R. Schmitt, and A. Weckenmann, Industrial applications of computed tomography, CIRP Ann. 63 (2014), no. 2, 655-677. https://doi.org/10.1016/j.cirp.2014.05.011
  5. N. O'Brien, M. Mavrogordato, R. Boardman, I. Sinclair, S. Hawker, and T. Blumensath, Comparing cone beam laminographic system trajectories for composite NDT, Case Stud. Nondestruct. Test. Evaluation. 6 (2016), no. Part B, 56-61. https://doi.org/10.1016/j.csndt.2016.05.004
  6. H. Deyhle, H. Towsyfyan, A. Biguri, M. Mavrogordato, R. Boardman, and T. Blumensath, Spatial resolution of a laboratory based X-ray cone-beam laminography scanning system for various trajectories, NDT E Int. 111 (2020). https://doi.org/10.1016/j.ndteint.2020.102222
  7. W. E. I. Long, An industrial computed laminography imaging system, (Proc. Digital Industrial Radiology and Computed Tomography, Belgium, Ghent), 2015, pp. 22-25.
  8. X. Pan, E. Y. Sidky, and M. Vannier, Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? Inverse Probl. 25 (2009), no. 12, 1230009.
  9. L. Yenumula, U. Kumar, and A. Dash, X-ray industrial computed laminography (ICL) simulation study of planar objects: optimization of laminographic angle, (Proc. Indian National Seminar & Exhibition on Nondestructive Evaluation NDE, Hyderabad, India), 2015.
  10. L. A. Feldkamp, L. C. Davis, and J. W. Kress, Practical conebeam algorithm, J. Opt. Soc. Am. A 1 (1984), no. 6, 612-619. https://doi.org/10.1364/JOSAA.1.000612
  11. J. M. Que, D. Q. Cao, W. Zhao, X. Tang, C. L. Sun, Y. F. Wang, C. F. Wei, R. J. Shi, L. Wei, Z. Q. Yu, and Y. L. Yan, Computed laminography and reconstruction algorithm, Chin. Phys. C. 36 (2012), no. 8, 777. https://doi.org/10.1088/1674-1137/36/8/017
  12. M. Yang, J. Zhu, Q. Liu, S. Duan, L. Liang, X. Li, W. Liu, and F. Meng, A practical method to calibrate the slant angle of central X-ray for laminography scanning system, NDT E Int. 64 (2014), 13-20. https://doi.org/10.1016/j.ndteint.2014.02.004
  13. M. Yang, Z. Li, L. Liang, X. Li, W. Liu, and Z. Gui, Automatic measurement of rotation center for laminography scanning system without dedicated phantoms, J. Electron. Imaging. 23 (2014), no. 5, 53018. https://doi.org/10.1117/1.JEI.23.5.053018
  14. M. J. Daly, J. H. Siewerdsen, Y. B. Cho, D. A. Jaffray, and J. C. Irish, Geometric calibration of a mobile C-arm for intraoperative cone-beam CT, Med. Phys. 35 (2008), no. 5, 2124-2136. https://doi.org/10.1118/1.2907563
  15. N. Navab, A. Bani-Hashemi, M. S. Nadar, K. Wiesent, P. Durlak, T. Brunner, K. Barth, and R. Graumann, 3D reconstruction from projection matrices in a C-arm based 3D-angiography system, (Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention, Cambridge, MA, USA), 1998, pp. 119-129.
  16. K. Wiesent, K. Barth, N. Navab, P. Durlak, T. Brunner, O. Schuetz, and W. Seissler, Enhanced 3-D-reconstruction algorithm for C-arm systems suitable for interventional procedures, IEEE Trans. Med. Imaging 19 (2000), no. 5, 391-403. https://doi.org/10.1109/42.870250
  17. R. R. Galigekere, K. Wiesent, and D. W. Holdsworth, Conebeam reprojection using projection-matrices, IEEE Trans. Med. Imaging 22 (2003), no. 10, 1202-1214. https://doi.org/10.1109/TMI.2003.817787
  18. F. Maes, D. Vandermeulen, and P. Suetens, Medical image registration using mutual information, Proc. IEEE 91 (2003), no. 10, 1699-1722. https://doi.org/10.1109/JPROC.2003.817864
  19. S. T. Kim, D. H. Kim, and Y. M. Ro, Generation of conspicuityimproved synthetic image from digital breast tomosynthesis, (Proc. 19th International Conference on Digital Signal Processing, Hong Kong, China), 2014, pp. 395-399.
  20. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process. 13 (2004), no. 4, 600-612. https://doi.org/10.1109/TIP.2003.819861
  21. M. Yang, J. Zhang, M. Yuan, X. Li, W. Liu, F. Meng, S. J. Song, and D. Wei, Calibration method of projection coordinate system for X-ray cone-beam laminography scanning system, NDT E Int. 52 (2012), 16-22. https://doi.org/10.1016/j.ndteint.2012.08.005
  22. K. Yang, A. L. C. Kwan, D. W. F. Miller, and J. M. Boone, A geometric calibration method for cone beam CT systems, Med. Phys. 33 (2006), no. 6-1, 1695-1706. https://doi.org/10.1118/1.2198187
  23. B. G. Chae and S. Lee, Sparse-view CT image recovery using two-step iterative shrinkage-thresholding algorithm, ETRI J. 37 (2015), no. 6, 1251-1258. https://doi.org/10.4218/etrij.15.0115.0401
  24. M. J. Willemink and P. B. Noel, The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence, Eur. Radiol. 29 (2019), no. 5, 2185-2195. https://doi.org/10.1007/s00330-018-5810-7