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Optimized Estimation of Scattered Radiation for X-ray Image Improvement: Realistic Simulation

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Abstract

Image processing algorithms for compensation of the scattered radiation influence in X-ray imaging are proposed, studied and optimized by numerical simulation. These algorithms include the scattering estimation by convolution (superposition) technique, estimation of kernel functions by Monte Carlo (MC) simulation, the determination of the optimal number and shape of kernel functions and image segmentation. The determination of the number and shape of kernel functions was performed by the MC simulation of the realistic Zubal phantom and the clustering analysis of shape features of kernel functions. Testing simulation study of the algorithms for chest images at 75 keV proves that the optimal number of kernel functions is equal to 8. This number provides the three-fold contrast enhancement without using the anti-scatter grids. The achieved contrast is about 95% of the primary image contrast that exceeds contrast enhancements achieved with anti-scatter grids. An increased number of used kernel functions provides a better image contrast and better resolution of scattered radiation image, but estimation errors also increase due to the segmentation and deconvolution errors.

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References

  1. Z. Song, A. M. Fendrick, D. G. Safran, B. E. Landon, M. E. Chernew, "Global budgets and technology-intensive medical services," Healthcare, v.1, n.1–2, p.15 (2013). DOI: https://doi.org/10.1016/j.hjdsi.2013.04.003.

    Article  Google Scholar 

  2. A. Assmus, "Early history of X rays," Beam Line, v.25, n.2, p.10 (1995). URI: https://www.slac.stanford.edu/pubs/beamline/25/2/25-2-assmus.pdf.

    Google Scholar 

  3. M. J. Jensen, J. E. Wilhjelm, X-ray imaging: Fundamentals and planar imaging (DTU, Nutech, 2014).

    Google Scholar 

  4. P. Monnin, F. R. Verdun, H. Bosmans, S. R. Pérez, N. W. Marshall, "A comprehensive model for x-ray projection imaging system efficiency and image quality characterization in the presence of scattered radiation," Phys. Med. Biol., v.62, n.14, p.5691 (2017). DOI: https://doi.org/10.1088/1361-6560/aa75bc.

    Article  Google Scholar 

  5. M. V. Kononov, O. A. Nagulyak, A. V. Netreba, "Influence of X-radiation in receiver system on reconstruction performance of projection tomography," Radioelectron. Commun. Syst., v.51, n.3, p.163 (2008). DOI: https://doi.org/10.3103/S0735272708030084.

    Article  Google Scholar 

  6. S. Webb, Webb’s Physics of Medical Imaging (CRC Press, Boca Raton, 2012). URI: https://www.routledge.com/Webbs-Physics-of-Medical-Imaging/Flower/p/book/9780750305730.

    Google Scholar 

  7. I. Šabič, D. Ključevšek, M. Thaler, D. Žontar, "The effect of anti-scatter grid on radiation dose in chest radiography in children," Cent. Eur. J. Paediatr., v.12, n.1, p.75 (2016). URI: http://cejpaediatrics.com/index.php/cejp/article/view/273/pdf.

    Google Scholar 

  8. E.-P. Rührnschopf, K. Klingenbeck, "A general framework and review of scatter correction methods in cone beam CT. Part 2: Scatter estimation approaches," Med. Phys., v.38, n.9, p.5186 (2011). DOI: https://doi.org/10.1118/1.3589140.

    Article  Google Scholar 

  9. W. Zhao, S. Brunner, K. Niu, S. Schafer, K. Royalty, G.-H. Chen, "A patient-specific scatter artifacts correction method," in Progress in Biomedical Optics and Imaging - Proceedings of SPIE. DOI: https://doi.org/10.1117/12.2043923.

    Chapter  Google Scholar 

  10. P. G. F. Watson, E. Mainegra-Hing, N. Tomic, J. Seuntjens, "Implementation of an efficient Monte Carlo calculation for CBCT scatter correction: phantom study," J. Appl. Clin. Med. Phys., v.16, n.4, p.216 (2015). DOI: https://doi.org/10.1120/jacmp.v16i4.5393.

    Article  Google Scholar 

  11. K. Kim, T. Lee, Y. Seong, J. Lee, K. E. Jang, J. Choi, Y. W. Choi, H. H. Kim, H. J. Shin, J. H. Cha, S. Cho, J. C. Ye, "Fully iterative scatter corrected digital breast tomosynthesis using GPU-based fast Monte Carlo simulation and composition ratio update," Med. Phys., v.42, n.9, p.5342 (2015). DOI: https://doi.org/10.1118/1.4928139.

    Article  Google Scholar 

  12. A. V. Netreba, S. P. Radchenko, M. O. Razdabara, "Correlation reconstructed spine and time relaxation spatial distribution of atomic systems in MRI," in 2014 IEEE 34th International Scientific Conference on Electronics and Nanotechnology (ELNANO) (IEEE). DOI: https://doi.org/10.1109/ELNANO.2014.6873453.

    Chapter  Google Scholar 

  13. Y. Suleimanov, S. Radchenko, O. Lefterov, A. Netreba, S. Vasnyov, V. Sava, J. Sanchez-Ramos, L. Prockop, R. Duara, "Magnetic resonance signal processing tool for diagnostic classification," in 2016 IEEE 36th International Conference on Electronics and Nanotechnology (ELNANO) (IEEE). DOI: https://doi.org/10.1109/ELNANO.2016.7493042.

    Chapter  Google Scholar 

  14. J. Maier, S. Sawall, M. Kachelriess, Y. Berker, "Deep scatter estimation (DSE): feasibility of using a deep convolutional neural network for real-time x-ray scatter prediction in cone-beam CT," in Medical Imaging 2018: Physics of Medical Imaging (SPIE). DOI: https://doi.org/10.1117/12.2292919.

    Chapter  Google Scholar 

  15. A. Y. Danyk, S. P. Radchenko, O. O. Sudakov, "Optimization of grid-less scattering compensation in X-ray imaging: Simulation study," in 2017 IEEE 37th International Conference on Electronics and Nanotechnology (ELNANO) (IEEE). DOI: https://doi.org/10.1109/ELNANO.2017.7939770.

    Chapter  Google Scholar 

  16. A. Danyk, S. Radchenko, A. Netreba, O. Sudakov, "Using clustering analysis for determination of scattering kernels in X-ray imaging," in 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) (IEEE). DOI: https://doi.org/10.1109/IDAACS.2019.8924353.

    Chapter  Google Scholar 

  17. E. D. Prilepsky, J. E. Prilepsky, "Estimation of optimal parameter of regularization of signal recovery," Radioelectron. Commun. Syst., v.61, n.9, p.406 (2018). DOI: https://doi.org/10.3103/S0735272718090030.

    Article  Google Scholar 

  18. I. A. Sushko, A. I. Rybin, "Speeding up the Tikhonov regularization iterative procedure in solving the inverse problem of electrical impedance tomography," Radioelectron. Commun. Syst., v.58, n.9, p.426 (2015). DOI: https://doi.org/10.3103/S0735272715090058.

    Article  Google Scholar 

  19. E.-P. Rührnschopf, K. Klingenbeck, "A general framework and review of scatter correction methods in x-ray cone-beam computerized tomography. Part 1: Scatter compensation approaches," Med. Phys., v.38, n.7, p.4296 (2011). DOI: https://doi.org/10.1118/1.3599033.

    Article  Google Scholar 

  20. I. G. Zubal, C. R. Harrell, E. O. Smith, Z. Rattner, G. Gindi, P. B. Hoffer, "Computerized three-dimensional segmented human anatomy," Med. Phys., v.21, n.2, p.299 (1994). DOI: https://doi.org/10.1118/1.597290.

    Article  Google Scholar 

  21. D. Sarrut, M. Bardiès, N. Boussion, N. Freud, S. Jan, J.-M. Létang, G. Loudos, L. Maigne, S. Marcatili, T. Mauxion, P. Papadimitroulas, Y. Perrot, U. Pietrzyk, C. Robert, D. R. Schaart, D. Visvikis, I. Buvat, "A review of the use and potential of the GATE Monte Carlo simulation code for radiation therapy and dosimetry applications," Med. Phys., v.41, n.6Part1, p.064301 (2014). DOI: https://doi.org/10.1118/1.4871617.

    Article  Google Scholar 

  22. O. Sudakov, M. Kononov, I. Sliusar, A. Salnikov, "User clients for working with medical images in Ukrainian Grid infrastructure," in 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS) (IEEE). DOI: https://doi.org/10.1109/IDAACS.2013.6663016.

    Chapter  Google Scholar 

  23. L. Scrucca, M. Fop, T. B. Murphy, A. E. Raftery, "mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models," R J., v.8, n.1, p.289 (2016). DOI: https://doi.org/10.32614/RJ-2016-021.

    Article  Google Scholar 

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We would like to thank the Ukrainian National Grid [22] Infrastructure and Information and the Computer Center of National Taras Shevchenko University of Kyiv for providing computing resources.

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  1. Taras Shevchenko National University of Kyiv, Kyiv, Ukraine

    A. Y. Danyk & O. O. Sudakov

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  1. A. Y. Danyk
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A. Y. Danyk and O. O. Sudakov

The authors declare that they have no conflict of interest.

The initial version of this paper in Russian is published in the journal “Izvestiya Vysshikh Uchebnykh Zavedenii. Radioelektronika,” ISSN 2307-6011 (Online), ISSN 0021-3470 (Print) on the link http://radio.kpi.ua/article/view/S0021347020080014 with DOI: https://doi.org/10.20535/S0021347020080014

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Danyk, A.Y., Sudakov, O.O. Optimized Estimation of Scattered Radiation for X-ray Image Improvement: Realistic Simulation. Radioelectron.Commun.Syst. 63, 387–397 (2020). https://doi.org/10.3103/S0735272720080014

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  • DOI: https://doi.org/10.3103/S0735272720080014

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