层间约束下三维块匹配的双能CT多成分分解

doi:10.3964/j.issn.1000-0593(2023)03-0774-07

Abstract
References
Related Citation (15)

Download: PDF (11363 KB)
Export: BibTeX | EndNote (RIS)

Abstract Dual-energy CT uses two groups of attenuation information under different energy spectra to accurately segment two kinds of basis materials. In practical applications, the internal material structure of the object is complex, and the composition is diversified. It is often necessary to obtain three or more basic material images to understand its internal structure information. Conventional CT is a continuous mixed spectral beam. The projection information obtained does not match the single-energy reconstruction algorithm, and there are errors in the attenuation coefficients of each basic material in the reconstructed image. The density of materials in the industrial field is generally larger and the noise of the basic material in the reconstructed image is more serious, affecting the accuracy of each component’s characterisation, especially for the materials with similar attenuation coefficients. In order to realize dual-energy data decomposition to obtain multiple high-quality basis images, in addition to the influence of noise in the reconstructed image, the selection of the coefficient matrix in the material decomposition model is also very important. However, there is a deviation between the attenuation coefficient value in the reconstructed image and the theoretical attenuation coefficient value. In the reconstructed image, the attenuation coefficients of different materials with similar densities are similar or even equal, resulting in the wrong selection of the material triplet of the pixel to be decomposed, which reduces the accuracy of material decomposition. Therefore, it proposes a 3D block-matching multi-component decomposition method with inter-layer constraints. In the method, a multi-material decomposition model with mass volume conservation and the constraint that each pixel comprises three types of materials at most is introduced. Three-dimensional structure similarity information is added to the selection of pixel material components. Constraint solution is carried out by utilizing the three-dimensional structure information to reduce noise pollution, and an initial basis image, roughly decomposed, is obtained; Then use the three-dimensional block matching method to match the initial basis image, and classify the three-dimensional feature constraints of each basis material image. After classification, select the optimal material composition triplet containing this type of basic material to carry out a multi-material decomposition model and obtain a more accurate component characterization diagram. In two groups of experiments on pure metal phantoms and granite. Compared with the results of the existing methods, the three-dimensional block matching decomposition method under interlayer constraints is more accurate in identifying industrial materials with similar attenuation coefficients, the structure of each component characterization image is complete, the image quality is good, and the detail processing is accurate. The quantitative analysis in pure metal phantom experiments shows that the PSNR and SSIM values of the proposed method are increased by 5%~6% and 31%~35%, respectively, compared with the existing methods. The effectiveness and robustness of the algorithm are verified, and more accurate multi-component decomposition is achieved under the conventional CT system.

Key words：Dual-energy CT; Multi-material decomposition; 3D block matching; Material triplets

Received: 2022-07-04 Accepted: 2022-10-24

ZTFLH:

O434.1

Corresponding Authors: CHEN Ping E-mail: pc0912@163.com

Cite this article:

KONG Xia,PAN Jin-xiao,ZHAO Xiao-jie, et al. Multi-Component Decomposition of 3D Block-Matched Dual-Energy CT With Interlayer Constraint[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 774-780.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2023)03-0774-07 OR https://www.gpxygpfx.com/EN/Y2023/V43/I03/774

[1] YAN Bin, LI Lei(闫镔，李磊). CT Image Reconstruction Algorithm(CT图像重建算法). Beijing: Science Press(北京: 科学出版社), 2014. 5.
[2] Alvarez R E, Macovski A. Physics in Medicine and Biology, 1976, 21(5): 733.
[3] 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.
[4] LI Lei，WANG Lin-yuan, CAI Ai-long, et al(李磊，王林元，蔡爱龙，等). Acta Optica Sinica(光学学报), 2016, 36(8)：0834001.
[5] Ducros N, Abascal J F P J, Sixou B, et al. Medical Physics, 2017, 44(9)：e174.
[6] ZHOU Zheng-dong, ZHANG Xu-ling, XIN Run-chao, et al(周正东，章栩苓，辛润超，等). Journal of Southeast University·Natural Science Edition(东南大学学报·自然科学版), 2020, 50(5)：935.
[7] LUO Ting, ZHAO Yun-song(罗婷，赵云松). Acta Optica Sinica(光学学报), 2020, 40(14)：1411001.
[8] ZHAO Yun-song, ZHANG Hui-tao, ZHAO Xing, et al(赵云松，张慧滔，赵星). Acta Electonica Sinica(电子学报), 2014, 42(4)：666.
[9] JIANG Jun-ru, YU Hai-jun, GONG Chang-cheng, et al(降俊汝，余海军，龚长城，等). Acta Optica Sinica(光学学报), 2020, 40(21)：2111004.
[10] WANG Wen-jie, CHEN Ping, PAN Jin-xiao, et al(王文杰，陈平，潘晋孝，等). Computerized Tomography Theory and Applications(CT理论与应用研究), 2021, 30(1)：61.
[11] Mendonca P R S, Lamb P, Sahani D V. IEEE Transactions on Medical Imaging, 2014, 33(1)：99.
[12] Xue Yi, Ruan Ruoshui, Hu Xiuhua, et al. Medical Physics, 2017, 44(3)：886.
[13] Xue Yi, Jiang Yangkang, Yang Chunlin, et al. IEEE Transactions on Computational Imaging，2019, 5(4)：515.
[14] 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)：1034001.
[15] LÜ Xian-jun(吕宪俊). Process Mineralogy(工艺矿物学). Changsha: Central South University Press(长沙: 中南大学出版社), 2011. 280.