Multi-Spectral CT Sequence DCM Fusion Algorithm Based on Priori Components
ZHAO Dan，CHEN Ping，HAN Yan，LI Yi-hong*
National Key Laboratory for Electronic Measurement Technology, Shanxi Key Laboratory of Signal Capturing & Processing, North University of China, Taiyuan 030051, China
Abstract：Multi-spectral CT imaging is to characterize the different components in the CT images of different spectral ranges. For more convenient displaying all components physical information in an image, it is necessary to study the fusion method of multi-spectral CT sequence. But the commonly used fusion methods, such as weighted average method and wavelet transform fusion method, are mainly for the optimization of image information. The physical properties of the components can not be expressed, so that the gray scale of the fused image without physical representation affects the quantitative detection of CT. A multi-spectral CT sequence DCM fusion algorithm based on priori components with physical characterization was presented. First, we got multi-spectrum projection sequence by Imaging method separated by energy spectrum filtering and the CT sequence with different energy spectrum can be obtained by TV-OSEM reconstruction algorithm. Second, the traditional DCM model and the improved DCM model were used to fuse the multi-spectral CT sequences. The traditional DCM model was strictly single energy, considering the non-strict monocular characteristic of the filtered energy spectrum. The fusion result can not accurately characterized all the components in the object sequence. To solve this problem, an improved DCM model was proposed. In the improved DCM model, a new voxel definition was selected and a metallic priori component was introduced as a reference substance in the multi-spectral CT sequence. The Prior component was used to calibrate other substances in the fusion results. Thus accurate distribution of each component in the CT sequence was achieved by calibration of the fusion results. Simulation result, the method can realize multi-spectral CT sequence fusion from the perspective of the physical representation. while satisfying the different components distinction of the CT sequence, the gray scale of its fused image had physical reference. This method is beneficial to the subsequent CT quantitative detection.
[1] NIU Su-yun, PAN Jin-xiao, CHEN Ping(牛素鋆, 潘晋孝, 陈 平). Acta Optica Sinica(光学学报), 2014, 34(10): 346.
[2] Lingenfelter D J, Fessler J A, Scott C D, et al. Nuclear Science IEEE Transactions on, 2011, 60(1): 204.
[3] Wang X, Meier D, Taguchi K, et al. Med. Phys.，2011, 38(3): 1534.
[4] XIAO Yu-huan, HUANG Lun-chun, LIU Xiao-yan, et al(肖玉环, 黄伦春, 刘晓燕, 等). Chinese Journal of Engineering Geophysics(工程地球物理学报), 2010, 7(2): 248.
[5] Wang W, Chang F. Journal of Computers, 2011, 6(12): 2559.
[6] MA Xian-xi, PENG Li, XU Hong(马先喜, 彭 力, 徐 红). Computer Engineering and Applications(计算机工程与应用), 2012, 48(8): 211.
[7] YU Wang-yang, CHEN Xiang-guang, DONG Shou-long, et al(余汪洋, 陈祥光, 董守龙, 等). Journal of Beijing Institute of Technology(北京理工大学学报), 2014, 34(12): 1262.
[8] Yang Y S, Liu K Y, Mayo S, et al. Journal of Petroleumence & Engineering, 2013, 105(3): 76.
[9] Wang Y, Yang Y, Xiao T, et al. International Journal of Geosciences, 2013, 4(2): 344.
[10] HUANG Tian-tian, CHEN Ping, PAN Jin-xiao, et al(黄甜甜, 陈 平, 潘晋孝, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(2): 503.
[11] Niu S, Ping C, Pan J, et al. Optik-International Journal for Light and Electron Optics, 2015, 126(2): 202.
[12] WANG Hai-peng, YANG Yu-shuang, JIANG Xing-jia, et al(王海鹏, 杨玉双, 蒋兴家, 等). Journal of ShanXi University·Natural Science Edition(山西大学学报·自然科学版), 2014, 37(4): 545.
