Blind Separation of Multi-Voltage Projection Sequence Based on Fundamental Effect Decomposition
ZHAO Yao-xia, HAN Yan, CHEN Ping*
School of Information and Communication Engineering, Shanxi Key Laboratory of Signal Capturing & Processing, North University of China，Taiyuan 030051, China
Abstract：In X-ray CT imaging system, the polychromatic X-ray results in beam hardening artifacts，which influence the material composition distinction. It can’t realize quantitative characterization. The multi-spectrum CT can reach the higher correspondence between the composition and image gray by narrow-energy-width or monoenergetic CT. Compared with traditional CT, spectral CT can distinguish the different component. The implement method based on the photon counting detector is limited in temporal resolution and spatial resolution. The implement method based on filter doesn’t have enough discrimination. And the implement method based on blind separation of multi-voltage projection sequences has better practicability. It guarantees the correspondence between the materials and the gray of reconstructed image. However, the attenuation coefficients are unknown and energy spectrum division is uncertain, the analytic energy value of reconstructed images is ambiguous. The error between the analytic energy and referenced energy is higher. Then it will influence the precision of the multi-component quantitative analysis. For this problem, an improved method is proposed. It utilizes the attenuation coefficient decomposition of compton effect and photoelectric effect as an energy constraint to eliminate the uncertainty energy partition. The error between the energy value of reconstructed images with decomposed projection and the reference energy value is reduced. In the decomposition model, the optimum object function is local variance sum of residual error minimum. The attenuation coefficient is decomposed as energy dependency term and material dependency term according to compton effect and photoelectric effect. The energy dependency term can be known in advance. It can be as energy constraint and used to fix the energy value of narrow-energy-width. Then the energy of decomposed projection is known and the energy of corresponding reconstructed images also is known. A cylinder composed of aluminum and silicon is used in the verification experiment since aluminium and silicon have approximate attenuation coefficients. The error between the attenuation coefficients of reconstructed images with the energy constraint is less than the result of reconstructed images without the energy constraint. The contrast tendency of silicon and aluminium with energy is close to the theoretical value. Also the difference with reference energy is reduced. The result shows that the proposed method solves the energy directivity problem of multi-spectrum CT based on the blind separation of multi-voltage projection sequences. The energy spectrum resolution ratio is higher. The composition representation is more accurate.
Key words：Multi-spectrum CT; Multi-voltage projection sequences; Blind separation; Narrow-spectrum projection; Energy order
[1] Fang L, Yin X, Wu L, et al. International Journal of Pharmaceutics, 2017, 531(2): 658.
[2] Ketcham R A, Hanna R D. Computers & Geosciences, 2014, 67(4): 49.
[3] HAO Jia, ZHANG Li, XING Yu-xiang, et al(郝 佳, 张 丽, 邢宇翔, 等). Journal of Tsinghua University·Science and Technology(清华大学学报·自然科学版), 2011, 51(4): 457.
[4] LI Bao-lei, ZHANG Ping-yu, LI Bin, et al(李保磊, 张萍宇, 李 斌, 等). Acta Optica Sinica(光学学报), 2017, 37(10): 1034001.
[5] Goodsitt M M, Christodoulou E G, Larson S C. Medical Physics, 2011, 38(4): 2222.
[6] CHEN Zhi-qiang, ZHANG Li, JIN Xin(陈志强, 张 丽, 金 鑫). Chines Science Bulletin(科学通报), 2017, 62(13): 1350.
[7] Wang S, Gao H, Zhang L, et al. Physics in Medicine & Biology, 2017, 62(6): 2208.
[8] NIU Su-su, PAN Jin-xiao, CHEN Ping(牛素鋆, 潘晋孝, 陈 平). Acta Optica Sinica(光学学报), 2014, 34(10): 1.
[9] Wei J, Han Y, Chen P. Measurement Science & Technology, 2016, 27(2): 025402.
[10] Wei J, Han Y, Chen P. International Journal of Biomedical Imaging, 2017, 2017，(2): 1.
[11] Gillis N, Glineur F. CORE Discussion Papers/Center for Operations Research and Econometrics, 2008, 1: 1.
