Research on Color Characterization of Material Components Based on Spectral CT
KONG Hui-hua1, 2, LIAN Xiang-yuan1, CHEN Ping2, PAN Jin-xiao1, 2
1. School of Science, North University of China, Taiyuan 030051, China
2. Shanxi Key Laboratory of Signal Capturing & Processing, Taiyuan 030051, China
Abstract：Photon-counting detector based X-ray spectral computed tomography (CT), realizes the transformation of CT image from gray to color by increasing energy resolution, which increases material identification capability. However, with increasing the number of energy channels, the channel’s noise increases significantly, which decreases the accuracy of material identification. In order to make full use of the sparsity of spectral CT images and the correlation between spectral CT images, a multi constraint narrow-spectral CT iterative reconstruction algorithm is proposed, which can effectively preserve the edges and details of the image while reducing the noise. Furthermore, principal component analysis (PCA) is used to estimate the spectrum information in narrow spectrum CT images, and the mapping relationship between principal component image and color components R, G, B are established. Finally, the color CT image is obtained. This method can effectively identify materials through the color representation of material components and reduce the background noise in the images. The results of simulation and practical experiments show the proposed reconstruction algorithm is effective, and it is feasible to use PCA for the color characterization of material components.
Key words：Spectral CT; Sparse representation; Correlation; Principal component analysis; Color characterization
孔慧华，连祥媛，陈 平，潘晋孝. 基于能谱CT的材料组分彩色表征研究[J]. 光谱学与光谱分析, 2021, 41(11): 3612-3617.
KONG Hui-hua, LIAN Xiang-yuan, CHEN Ping, PAN Jin-xiao. Research on Color Characterization of Material Components Based on Spectral CT. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3612-3617.
[1] Wu Weiwen, Yu Haijun, Chen Peijun, et al. Physics in Medicine and Biology, 2020, 65(24): 245006.
[2] Nicolas Ducros, Juan Felipe Perez-Juste Abascal, Bruno Sixou, et al. Medical Physics，2017, 44 (9), e174.
[3] Zhang Zheng, Chen Buxin, Xia Dan, et al. Medical Image Analysis, 2021, 70: 102030.
[4] Kanii Yoshinori, Ichikawa Yasutaka, Nakayama Ryohei, et al. Japanese Journal of Radiology, 2020, 38(3): 215.
[5] Wang Shaoyu, Wu weiwen, Feng Jian, et al. Physics in Medicine and Biology. 2020, 65(24): 245005.
[6] Dong Yiqiu, Hansen Per Christian, Kjer Hans Martin. IEEE Transactions on Computational Imaging, 2018, 4(4): 528.
[7] Niu Shanzhou, Yu Gaohang, Ma Jianhua et al. Inverse Problems, 2018, 34(2): 024003.
[8] Rigie David S，La Riviere Patrick J. Physics in Medicine and Biology，2015, 60 (5): 1741.
[9] Xie Huiqiao, Ren Yan, Long Wenting, et al. IEEE Transactions on Biomedical Engineering，2021, 68(3): 1074.
[10] Kong Huihua, Lei Xiaoxue, Lei Lei, et al. IEEE Access，2020, 8: 133367.
[11] Kong Huihua, Liu Rui，Yu Hengyong. Journal of X-ray Science and Technology，2016, 24(2): 221.
[12] Wang Miaoshi, Zhang Yanbo, Liu Rui, et al. Physics in Medicine & Biology, 2016, 61(24): 8699.
