International Journal of Social Science & Economic Research
Submit Paper

Title:
OPTIMIZATION ANALYSIS OF CT SYSTEM IMAGING

Authors:
Yingxia Liu , Hanjiang Dong, Lubin Wu, Feizheng Xu, Rui Deng

|| ||

Yingxia Liu , Hanjiang Dong, Lubin Wu, Feizheng Xu, Rui Deng
Mathematical modeling base, zhuhai campus, jinan university, China

MLA 8
Liu, Yingxia, et al. "OPTIMIZATION ANALYSIS OF CT SYSTEM IMAGING." Int. j. of Social Science and Economic Research, vol. 3, no. 11, Nov. 2018, pp. 6245-6259, ijsser.org/more2018.php?id=435. Accessed Nov. 2018.
APA
Liu, Y., Dong, H., Wu, L., Xu, F., & Deng, R. (2018, November). OPTIMIZATION ANALYSIS OF CT SYSTEM IMAGING. Int. j. of Social Science and Economic Research, 3(11), 6245-6259. Retrieved from ijsser.org/more2018.php?id=435
Chicago
Liu, Yingxia, Hanjiang Dong, Lubin Wu, Feizheng Xu, and Rui Deng. "OPTIMIZATION ANALYSIS OF CT SYSTEM IMAGING." Int. j. of Social Science and Economic Research 3, no. 11 (November 2018), 6245-6259. Accessed November, 2018. ijsser.org/more2018.php?id=435.

References
[1]. Kim, D. H., Sang, W. P., Kim, D. H., Yoo, M. S., & Lee, Y. (2018). Feasibility of sinogram reconstruction based on inpainting method with decomposed sinusoid-like curve (s-curve) using total variation (tv) noise reduction algorithm in computed tomography (ct) imaging system: a simulation study. Optik.
[2]. Wang, L., Zhang, H., Cai, A., Li, Y., Yan, B., & Li, L., et al. (2015). System matrix analysis for sparse-view iterative image reconstruction in x-ray ct. J Xray Sci Technol, 23(1), 1-10.
[3]. Do, S., Karl, W. C., Singh, S., Kalra, M., Brady, T., & Shin, E., et al. (2014). High fidelity system modeling for high quality image reconstruction in clinical ct. Plos One, 9(9), e111625.
[4]. Rowley, L. M., Bradley, K. M., Boardman, P., Hallam, A., & Mcgowan, D. R. (2017). Optimization of image reconstruction for 90y selective internal radiotherapy on a lutetium yttrium orthosilicate pet/ct system using a bayesian penalized likelihood reconstruction algorithm. Journal of Nuclear Medicine Official Publication Society of Nuclear Medicine, 58(4), 658.
[5]. Mo Hua, Long Lingli. Convolution back projection graphic method for X-CT image reconstruction [J]. Chinese Journal of Medical Physics, 1999, 16(3):143-145.
[6]. China Society for Industrial and Applied Mathematics. (2017). Higher Education Club Cup National Contest on Mathematical Modeling for College Students. http://www. mcm.edu.cn
[7]. Fan Huiyun. Research on CT Image Filtering Back Projection Reconstruction Algorithm [D]. Northwestern Polytechnical University,2007.
[8]. Yi Xiaofei, Chen Fujie, Yang Xuejun. Image template matching based on Wiener filtering [J]. Computer research and development,2000(12):1499-1503.
[9]. Liang Xiaoping, Luo Xiaoshu. Wiener filtering image deblurring algorithm based on genetic adaptation [J]. Journal of Guangxi Normal University (Natural Science Edition), 2017,35(4):17-23. DOI:10.16088/j.issn.1001-6600.2017.04.003.

Abstract:
Aiming at the problem of how to reconstruct an image based on the absorbed data from the unknown medium using the CT system with known calibration parameters, this paper proposed the Optimized filtered back-projection model of Image Denoising. This model first uses the Ramp-Lak filtered back-projection algorithm to reconstruct the image of the unknown medium to obtain the position and geometry of the medium. Then, considering the limitations of the single filtered back-projection algorithm, Shepp-Logan filtered back-projection algorithm and Lewitt filtered back-projection algorithm was used separately to reconstruct the CT image. Then the gray-scale variance function is used to calculate the sharpness of the above three groups of images. The image used the Ramp-Lak filtered back-projection algorithm got the highest score, indicating that the resolution is the best. Then, because the noise signal generated during the back-projection of the image will affect the sharpness of the image, the Wiener algorithm is used to denoise the image with the highest score above, so that the noise of the image is greatly reduced. A clearer image result is obtained, which more accurately determines the position and geometry of the unknown medium, and provides a reference value for imaging of the CT system.