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Full text (en) Physical bases for determination of scattering kernels from incomplete data in grid-less X-ray imaging
A. Yu. Danyk*, O. O. Sudakov
Medical Radiophysics Department, Faculty of Radiophysics, Electronics and Computer Systems, National Taras Shevchenko University of Kyiv, Kyiv, Ukraine
*Corresponding author. E-mail address:
antondanik@gmail.com
Abstract: A mathematical model for the determination of X-ray scattering kernels’ shapes based on incomplete simulation or measurement data was introduced and tested using a mathematical phantom. The model is originally intended for low-dose X-ray imaging without anti-scatter grids. The proposed model fits different kinds of symmetrical and asymmetrical scattering kernels in different tissues well enough for practical applications. Kernels asymmetry is mostly caused by irradiation of the object near the boundaries of different tissues. The model describes a variety of asymmetrical kernels by proposed “sectoral” members. Application of the proposed model in scattering compensation procedure reduces resulting error up to 50 % for “wide” scattering kernels.
Keywords: X-ray image, scattered X-ray radiation convolution kernels, clustering analysis, segmentation, Monte-Carlo simulation, approximation, incomplete data.
References:1. M.A. Flower (ed.). Webb's Physics of Medical Imaging. 2-nd ed. (CRC Press, 2012) 812 p. Google books
2. A. Danyk. The problem of scattered radiation in X-ray imaging. Bulletin of Taras Shevchenko National University of Kyiv. Ser.: Physics and Mathematics 1 (2018) 72. https://bphm.knu.ua/index.php/bphm/issue/view/57/2018_1
3. Z. Wei et al. A patient-specific scatter artifacts correction method. In: Proc. SPIE 9033. Medical Imaging 2014: Physics of Medical Imaging, San Diego, California, United States, 19 March 2014. https://doi.org/10.1117/12.2043923
4. K. Kim et al. Fully iterative scatter corrected digital breast tomosynthesis using GPU-based fast Monte Carlo simulation and composition ratio update. Medical Physics 42 (2015) 5342. https://doi.org/10.1118/1.4928139
5. J. Maier et al. Deep scatter estimation (DSE): feasibility of using a deep convolutional neural network for real-time x-ray scatter prediction in cone-beam CT. In: Proc. SPIE 10573. Medical Imaging 2018: Physics of Medical Imaging, Houston, Texas, United States, March 9, 2018. https://doi.org/10.1117/12.2292919
6. A. Danyk, S. Radchenko, O. Sudakov. Optimization of Grid-less Scattering Compensation in X-ray Imaging: Simulation Study. In: Proc. of the 37-th IEEE Intern. Conf. on Electronics and Nanotechnology (ELNANO), Kyiv, Ukraine, April 18 - 20, 2017. P. 316. https://doi.org/10.1109/ELNANO.2017.7939770
7. A. Danyk et al. Using clustering analysis for determination of scattering kernels in X-ray imaging. In: Proc. of the 10-th IEEE Intern. Conf. on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS, Metz, France, September 18 - 21, 2019. P. 211. https://doi.org/10.1109/IDAACS.2019.8924353
8. A. Danyk, O. Sudakov. Optimized Estimation of Scattered Radiation for X-ray Images Improvement: Realistic Simulation. Radioelectronics and Communications Systems 63 (2020) 387. https://doi.org/10.3103/S0735272720080014
9. J.P. Shah, S.D. Mann, M.P. Tornai. Characterization of X-ray scattering for various phantoms and clinical breast geometries using breast CT on a dedicated hybrid system. Journal of X-ray Science and Technology 25 (2017) 373. https://doi.org/10.3233/XST-16202
10. J.L. Ducote, S. Molloi. Scatter correction in digital mammography based on image deconvolution. Physics in Medicine & Biology 55 (2010) 1295. https://doi.org/10.1088/0031-9155/55/5/003
11. J.M. Boone, J.A. Seibert. An analytical model of the scattered radiation distribution in diagnostic radiology. Medical Physics 15 (1988) 721. https://doi.org/10.1118/1.596186
12. M. Honda, K. Kikuchi, K. Komatsu. Method for estimating the intensity of scattered radiation using a scatter generation model. Medical Physics 18 (1991) 219. https://doi.org/10.1118/1.596710
13. W. Yao, K.W. Leszczynski. An analytical approach to estimating the first order x-ray scatter in the heterogenous medium. Medical Physics 36 (2009) 3145. https://doi.org/10.1118/1.3152114
14. A. Liemert, A. Kienle. Exact and efficient solution of the radiative transport equation for the semi-infinite medium. Scientific Reports 3 (2013) 1. https://doi.org/10.1038/srep02018
15. D.M. Paganin, S.M. Kaye. X-ray Fokker-Planck equation for paraxial imaging. Scientific Reports 9 (2019) 1. https://doi.org/10.1038/s41598-019-52284-5
16. D. Sarrut et al. A review of the use and potential of the GATE Monte Carlo code for radiation therapy and dosimetry applications. Medical Physics 41(6) (2014) 064301. https://doi.org/10.1118/1.4871617
17. O. Sudakov et al. User Clients for Working with Medical Images in Ukrainian Grid Infrastructure. In: Proc. of the 7-th IEEE Intern. Conf., IDAACS, Berlin, Germany, September 12 - 14, 2013. P. 705. https://doi.org/10.1109/IDAACS.2013.6663016