An Algorithm of Electrical Impedance Tomography Based on Differential Iteration
Zhang Weirui1, Zhang Tao1,2, Shi Xuetao1, Fu Feng1, Xu Canhua1
1. Department of Biomedical Engineering The Fourth Military Medical University Xi'an 710032 China; 2. Drug and Instrument Supervision and Inspection Station Xining Joint Logistics Support Center Lanzhou 730050 China
Abstract:Most electrical impedance tomography algorithms need to select optimal regula- rization parameters to overcome the ill-conditioned equation and obtain better image quality. This paper proposes an electrical impedance tomography algorithm based on differential iteration, which uses dynamic linear approximation to improve imaging quality without adjusting the regularization parameters. A disturbance model is established in the approximate linear region, and the gradient method is used to derive the conductivity differential iterative relationship for rapid reconstruction. Then, the image reconstructed by this algorithm is compared with the reconstructed image based on several objective regularization parameter selection methods in terms of position error, resolution, shape deformation, ringing, and so on. The simulation results show that the proposed differential iterative algorithm can maintain similar or better results with the commonly used reconstruction algorithms, and has good practical application prospects.
章伟睿, 张涛, 史学涛, 付峰, 徐灿华. 基于差分迭代的电阻抗成像算法研究[J]. 电工技术学报, 2021, 36(4): 747-755.
Zhang Weirui, Zhang Tao, Shi Xuetao, Fu Feng, Xu Canhua. An Algorithm of Electrical Impedance Tomography Based on Differential Iteration. Transactions of China Electrotechnical Society, 2021, 36(4): 747-755.
[1] 徐灿华, 董秀珍. 生物电阻抗断层成像技术及其临床研究进展[J]. 高电压技术, 2014, 40(12): 3738-3745. Xu Canhua, Dong Xiuzhen.Advancements in elec- trical impedance tomography and its clinical appli- cations[J]. High Voltage Engineering, 2014, 40(12): 3738-3745. [2] 董秀珍. 生物电阻抗成像研究的现状与挑战[J]. 中国生物医学工程学报, 2008(5): 641-643, 649. Dong Xiuzhen.Recent progress and challenges in the study of bioimpedance imaging[J]. Chinese Journal of Biomedical Engineering, 2008(5): 641-643, 649. [3] 张涛, 安志伟, 刘学超, 等. 脑部电阻抗有限元模型的脑膜结构仿真[J]. 医疗卫生装备, 2020, 41(4): 35-38. Zhang Tao, An Zhiwei, Liu Xuechao, et al.Brain EIT finite element simulation involving cerebral pia mater structure[J]. Chinese Medical Equipment Journal, 2020, 41(4): 35-38. [4] 张帅, 李子秀, 张雪莹, 等. 基于时间反演的磁动力超声成像仿真与实验[J]. 电工技术学报, 2019, 34(16): 3303-3310. Zhang Shuai, Li Zixiu, Zhang Xueying, et al.The simulation and experiment of magneto-motive ultrasound imaging based on time reversal method[J]. Transactions of China Electrotechnical Society, 2019, 34(16): 3303-3310. [5] 张帅, 崔琨, 史勋, 等. 经颅磁声电刺激参数对神经元放电模式的影响分析[J]. 电工技术学报, 2019, 34(18): 3741-3749. Zhang Shuai, Cui Kun, Shi Xun, et al.Effect analysis of transcranial magneto-acousto-electrical stimulation parameters on neural firing patterns[J]. Transactions of China Electrotechnical Society, 2019, 34(18): 3741-3749. [6] 李文慧, 姜慧, 杨帆, 等. 高频高压激励环形表面介质阻挡放电特性实验研究[J]. 电工技术学报, 2020, 35(16): 3539-3550. Li Wenhui, Jiang Hui, Yang Fan, et al.Experimental study on ring surface dielectric barrier discharge characteristics of high frequency and high voltage excitation[J]. Transactions of China Electrotechnical Society, 2020, 35(16): 3539-3550. [7] 赵宏晨, 刘晓明, 杨滢璇, 等. 基于分数阶Tikhonov正则化方法的电弧反演研究[J]. 电工技术学报, 2019, 34(1): 84-91. Zhao Hongchen, Liu Xiaoming, Yang Yingxuan, et al.Research on arc inversion based on fractional Tikhonov regularization method[J]. Transactions of China Electrotechnical Society, 2019, 34(1): 84-91. [8] Graham B M, Adler A.Objective selection of hyper- parameter for EIT[J]. Physiological Measurement, 2006, 27(5): 65-79. [9] Hansen P C, Jensen T K, Rodriguez G.An adaptive pruning algorithm for the discrete L-curve criterion[J]. Journal of Computational & Applied Mathematics, 2015, 198(2): 483-492. [10] Xue Feng, Blu T, Liu Jiaqi, et al.A novel GCV-based criterion for parameter selection in image deconvo- lution[C]//IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, 2018: 1403-1407. [11] Adler A, Guardo R.Electrical impedance tomography: regularized imaging and contrast detection[J]. IEEE Transactions on Medical Imaging, 1996, 15(2): 170-179. [12] Liu Xuechao, Li Haoting, Ma Hang, et al.An iterative damped least-squares algorithm for simu- ltaneously monitoring the development of hemorrhagic and secondary ischemic lesions in brain injuries[J]. Medical & Biological Engineering & Computing, 2019, 57(6): 1917-1931. [13] 王琦, 彭圆圆, 汪剑鸣, 等. 动态电阻抗成像时空相关性重建方法研究[J]. 电子测量与仪器学报, 2018, 32(2): 153-160. Wang Qi, Peng Yuanyuan, Wang Jianming, et al.Research on spatio-temporal relativity reconstruction method in dynamic electrical impedance tomo- graphy[J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(2): 153-160. [14] Ho S L, Yang Shiyou.A computationally efficient vector optimizer using ant colony optimizations algorithm for multobjective designs[J]. IEEE Transa- ctions on Magnetics, 2008, 44(6): 1034-1037. [15] 夏慧, 刘国强, 黄欣, 等. 注入电流式磁声成像平面模型的逆问题研究[J]. 电工技术学报, 2017, 32(4): 147-153. Xia Hui, Liu Guoqiang, Huang Xin, et al.The inverse problem study of plane model based on magneto- acoustic tomography with current injection[J]. Transa- ctions of China Electrotechnical Society, 2017, 32(4): 147-153. [16] 李翠环, 汪友华, 耿读艳, 等. 基于双参数模型的ECT图像重构混合算法[J]. 电工技术学报, 2012, 27(4): 24-29. Li Cuihuan, Wang Youhua, Geng Duyan, et al.Hybrid algorithm based on two-parameter model for electrical capacitance tomography image reconstru- ction[J]. Transactions of China Electrotechnical Society, 2012, 27(4): 24-29. [17] Adler A, Arnold J H, Bayford R, et al.GREIT: a unified approach to 2D linear EIT reconstruction of lung images[J]. Physiological Measurement, 2009, 30(6): S35-S55. [18] 李彦东, 杨滨, 徐灿华, 等. 针对脑部电阻抗成像的四种正则参数选取方法的比较研究[J]. 中国医学装备, 2013, 10(11): 4-7. Li Yandong, Yang Bin, Xu Canhua, et al.Research on comparison of four types of regularization parameter choosing methods for brain electrical impedance tomography[J]. China Medical Equipment, 2013, 10(11): 4-7.