Focused algorithms of X-ray imaging on the basis of multi-line scanning

Ke Li, Bin Liu, Lipeng Wang, Xinyu Zhang, Jixing Guo

Article ID: 1738
Vol 4, Issue 1, 2021, Article identifier:72-83

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Abstract


In the process of X-ray transmission imaging, the mutual occlusion between structures will lead to the image information overlap, and the computed tomography (CT) method is often required to obtain the structure information at different depths, but with low efficiency. To address these problems, an X-ray focused on imaging algorithm based on multi-line scanning is proposed, which only requires the scene target to pass through the detection area along a straight line to extract multi-view information, and uses the optical field reconstruction theory to achieve the de-obscured reconstruction of the structure at a specified depth with high real-time. The results of multi-line scan and X-ray reconstruction of the target show that the proposed method can reconstruct the information of any specified depth layer, and it can perform fast imaging detection of the mutually occluded target structures and improve the recognition of the occluded targets, which has a good application prospect.


Keywords


Imaging System; Focusing Imaging; X-ray; Multi-line Scanning; De-overlapping

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References


Gou L, Wang X, Cao H. The present state and future development of X-ray imaging technology. Journal of Chengdu Institute of Technology 2002; 29(2): 227–231.

Li B, Tang S, Yang J, et al. Clinical application, research progress of CT spectral imaging. Chinese Journal of Cardiovascular Rehabilitation Medicine 2015; 24(3): 343–346.

Zhou S, Brahme A. Development of phase-contrast X-ray imaging techniques and potential medical applications. Physica Medica, 2008; 24(3): 129–148.

Hao J, Zhang L, Chen Z, et al. Multi-energy X-ray imaging technique and its application in computed tomography. Computerized Tomography Theory and Applications 2011; 20(1): 141–150.

Hounsfield GN. Computerized transverse axial scanning (tomography): Part 1. Description of system. The British Jornal of Radiology 1973; 46(552): 1016–1022.

Qi J, Chen R, Liu B, et al. Grating based X-ray phase contrast CT imaging with iterative reconstruction algorithm. Acta Physica Sinica 2017; 66(5): 054202.

Sidky EY, Pan X. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Physics in Medicine & Biology 2008; 53(17): 4777–4807.

Sidky EY, Kao CM, Pan X. Accurate image reconstruction in CT from projection data taken at few-views. Medical Imaging 2006: Physics of Medical Imaging 2006; 6142: 614229.

Gondrom S, Zhou J, Maisl M, et al. X-ray computed laminography: An approach of computed tomography for applications with limited access. Nuclear Engineering and Design 1999; 190(1/2): 141–147.

Wan X, Liu X, Wu Z. Review of computed laminography. CT Theory and Applications 2014; 23(5): 883–892.

Xu F, Helfen L, Baumbach T, et al. Comparison of image quality in computed laminography and tomography. Optics Express 2012; 20(2): 794.

Ng R. Fourier slice photography. ACM Transactions on Graphic 2005; 24(3): 735.

Liu Y, Liu B, Pan J. Synthetic aperture imaging algorithm via foreground removing. Acta Optica Sinica 2018; 38(6): 0611002.

Berry MV, Klein S. Integer, fractional and fractal Talbot effects. Journal of Modern Optics 1996; 43(10): 2139–2164.

Zhou Z. Research on light field imaging technology [PhD thesis]. Hefei: University of Science and Technology of China; 2012.

Nie Y, Xiang L, Zhou Z. Advances in light field photography technique. Journal of the Graduate School of the Chinese Academy of Science 2011; 28(5): 563–572.

Wilburn B, Joshi N, Vaish V, et al. High performance imaging using large camera arrays. ACM Transactions on Graphic 2005; 24(3): 765–776.

Vaish V, Wilburn B, Joshi N, et al. (editors). Using plane+ parallax for calibrating dense camera arrays. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2004 Jun 27–Jul 2; Washington DC. New York: IEEE; 2004. p. 8161383.

Yu D, Tan H. Engineering optics. Beijing: China Machine Press; 2011.

Vaish V, Garg G, Talvala E, et al. (editors). Synthetic aperture focusing using a shear-warp factorization of the viewing transform. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)-Workshops; 2005 Sept 21–23; State of California. New York: IEEE; 2006.

Chuang Y, Curless B, Salesin DH, et al. (editors). A Bayesian approach to digital matting. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2001 Dec 8–14; Hawaii State. New York: IEEE; 2001. p. 7184431.

Liu B, Pan Y, Yan W. Defocusing mechanism and focusing evaluation function of light field imaging. Acta Physica Sinica 2019; 68(20): 204202.

Levin A, Lischinski D, Weiss Y. A closed-form solution to natural image matting. IEEE Transactions on Pattern Analysis and Machine Intelligence 2008; 30(2): 228–242.

Chen T, Wang Y, Schillings V, et al. (editors). Grayscale image matting and colorization. Asian Conference on Computer Vision; 2004 Jan 27–30; Korea. Korea: ACCV; 2004. p. 1164–1169.




DOI: http://dx.doi.org/10.24294/irr.v4i1.1738

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