Research of electrical impedance tomography based on multilayer artificial neural network optimized by Hadamard product for human-chest models_Journal of Biomedical Engineering

Authors：

SONG Zhenzhong ¹ ,  LI Jianping ¹ , WEN Jianming ¹ , WAN Nen ¹ , MA Jijie ¹ , ZHANG Yu ¹ , HU Yili ¹ ,  GAO Zengfeng ^1,2

1. The Key Laboratory of Intelligent Operation and Maintenance Technology & Equipment for Urban Rail Transit of Zhejiang Province, Institute of Precision Machinery and Smart Structure, College of Engineering, Zhejiang Normal University, Jinhua, Zhejiang 321004, P. R. China;
2. Graduate School of Science and Engineering, Chiba University, Chiba 263-8522, Japan;

Corresponding author：

LI Jianping, Email: lijp@zjnu.cn; GAO Zengfeng, Email: zengfenggao@gmail.com

Keywords：

Electrical impedance tomography; Multilayer artificial neural network; Hadamard product; Artifacts

DOI：

10.7507/1001-5515.202305047

Video：

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Electrical impedance tomography (EIT) is a non-radiation, non-invasive visual diagnostic technique. In order to improve the imaging resolution and the removing artifacts capability of the reconstruction algorithms for electrical impedance imaging in human-chest models, the HMANN algorithm was proposed using the Hadamard product to optimize multilayer artificial neural networks (MANN). The reconstructed images of the HMANN algorithm were compared with those of the generalized vector sampled pattern matching (GVSPM) algorithm, truncated singular value decomposition (TSVD) algorithm, backpropagation (BP) neural network algorithm, and traditional MANN algorithm. The simulation results showed that the correlation coefficient of the reconstructed images obtained by the HMANN algorithm was increased by 17.30% in the circular cross-section models compared with the MANN algorithm. It was increased by 13.98% in the lung cross-section models. In the lung cross-section models, some of the correlation coefficients obtained by the HMANN algorithm would decrease. Nevertheless, the HMANN algorithm retained the image information of the MANN algorithm in all models, and the HMANN algorithm had fewer artifacts in the reconstructed images. The distinguishability between the objects and the background was better compared with the traditional MANN algorithm. The algorithm could improve the correlation coefficient of the reconstructed images, and effectively remove the artifacts, which provides a new direction to effectively improve the quality of the reconstructed images for EIT.

Citation： SONG Zhenzhong, LI Jianping, WEN Jianming, WAN Nen, MA Jijie, ZHANG Yu, HU Yili, GAO Zengfeng. Research of electrical impedance tomography based on multilayer artificial neural network optimized by Hadamard product for human-chest models. Journal of Biomedical Engineering, 2024, 41(3): 439-446, 454. doi: 10.7507/1001-5515.202305047 Copy

1.	Costa E L V, Gonzalez Lima R, Amato M B P. Electrical impedance tomography. Curr Opin Crit Care, 2009, 15(1): 18-24..
2.	Song Zhenzhong, Wen Jianming, Wan Nen, et al. An optimization algorithm H-GVSPM for electrical impedance tomography. IEEE Sens J, 2023, 23(5): 4518-4526..
3.	王子辰, 付荣, 张新宇, 等. 基于Deep CG的肺部电阻抗成像方法. 中国生物医学工程学报, 2023, 42(2): 148-157..
4.	叶明, 李晓丞, 刘凯, 等. 一种基于U～2-Net模型的电阻抗成像方法. 仪器仪表学报, 2021, 42(2): 235-243..
5.	孙敏, 贾辉, 秦卿雯, 等. 电阻抗成像原位在线监测超滤膜污染行为研究. 化工学报, 2022, 73(4): 1754-1762, 1847..
6.	Gallo G J, Thostenson E T. Spatial damage detection in electrically anisotropic fiber-reinforced composites using carbon nanotube networks. Compos Struct, 2016, 141: 14-23..
7.	Bao Huilu, Li Jianping, Wen Jianming, et al. Quantitative evaluation of burn injuries based on electrical impedance spectroscopy of blood with a seven-parameter equivalent circuit. Sensors, 2021, 21(4): 1496..
8.	陈晓艳, 吴君, 王化祥, 等. 不同体位呼吸过程的电阻抗断层成像研究. 生物医学工程学杂志, 2010, 27(5): 1004-1007..
9.	王慧泉, 尹剑利, 李光旭, 等. 磁探测电阻抗成像呼吸监测仿真研究. 生物医学工程学杂志, 2017, 34(1): 27-33..
10.	张静, 刘晓姝, 滕鸿, 等. 应用电阻抗断层成像技术评价神经调节辅助通气和压力支持通气模式对慢性阻塞性肺疾病急性加重患者的影响. 上海医学, 2020, 43(5): 299-301..
11.	Sella N, Pettenuzzo T, Zarantonello F, et al. Electrical impedance tomography: a compass for the safe route to optimal PEEP. Resp Med, 2021, 187(11): 106555..
12.	Shono A, Kotani T, Inéz Frerichs. Personalisation of therapies in COVID-19 associated acute respiratory distress syndrome, using electrical impedance tomography. J Crit Care Med (Targu Mures), 2021, 7(1): 62-66..
13.	Gibot S, Conrad M, Courte G, et al. Positive end-expiratory pressure setting in COVID-19-related acute respiratory distress syndrome: comparison between electrical impedance tomography, PEEP/FiO2 tables, and transpulmonary pressure. Front Med (Lausanne), 2021, 8: 720920..
14.	Zamani M, Kallio M, Bayford R, et al. Generation of anatomically inspired Human Airway Tree using electrical impedance tomography: a method to estimate regional lung filling characteristics. IEEE T Med Imaging, 2022, 41(5): 1125-1137..
15.	李芳, 方海东, 吴阳, 等. 面向肺功能检测的便携式电阻抗成像系统开发. 机械制造与自动化, 2022, 51(5): 239-242, 246..
16.	李星, 杨帆, 余晓, 等. 基于自诊断正则化的电阻抗成像逆问题研究. 生物医学工程学杂志, 2018, 35(3): 460-467..
17.	曹璐, 杨滨, 李昊庭, 等. 一种基于频谱约束的多频动态电阻抗断层成像算法. 生物医学工程学杂志, 2020, 37(1): 80-86..
18.	丁明亮, 李晓童, 卢立晖. 基于邻域信息和快速FCM的肺部电阻抗成像伪迹优化算法. 电子与信息学报, 2022, 44(9): 3320-3327..
19.	Wu Yang, Chen Bai, Liu Kai, et al. Bayesian image reconstruction using weighted Laplace prior for lung respiratory monitoring with electrical impedance tomography. IEEE T Instrum Meas, 2023, 72: 1-11..
20.	Wu Yang, Chen Bai, Liu Kai, et al. Shape reconstruction with multiphase conductivity for electrical impedance tomography using improved convolutional neural network method. IEEE Sens J, 2021, 21(7): 9277-9287..
21.	Zhu L, Lu W, Soleimani M, et al. Electrical impedance tomography guided by digital twins and deep learning for lung monitoring. IEEE T Instrum Meas, 2023, 72: 1-9..
22.	Wang Peng, Xie Lili, Sun Yicai. Electrical impedance tomography based on BP neural network and improved PSO// 2009 International Conference on Machine Learning and Cybernetics. Hebei: IEEE, 2009: 1059-1064..
23.	戎舟, 李若愚, 方滔. 基于多层人工神经网络的电阻抗成像算法. 国外电子测量技术, 2021, 40(1): 80-86..
24.	郑皓楠. 电阻抗成像激励测量方式和成像方法研究. 电子测试, 2021(5): 43-45..
25.	蔡荣辉, 崔雨轩, 薛培静. 三层BP神经网络隐层节点数确定方法探究. 电脑与信息技术, 2017, 25(5): 29-33..
26.	巴斌, 刘国春, 李韬, 等. 基于哈达玛积扩展子空间的到达时间和波达方向联合估计. 物理学报, 2015, 64(7): 384-392..
27.	Xu Yangyang, Li Yaotao, Li Zhengbo. Some results on the Hadamard product of tensors. Bull Iran Math Soc, 2019, 45(4): 1193-1219..
28.	Dong G Y, Endo H, Hayano S, et al. GVSPM for reconstruction in electrical impedance tomography. IEEE T Magn, 2003, 39(3): 1630-1633..
29.	Takei M, Saito Y. Application of the generalized vector sampled pattern matching method to reconstruction of electrical capacitance CT images. Meas Sci Technol, 2004, 15(7): 1371-1381..
30.	Xu Zhen, Huang Junchao, Jiang Yandan, et al. An image reconstruction algorithm for a 12-electrode capacitively coupled electrical resistance tomography system under 2-electrode excitation strategy. IEEE T Instrum Meas, 2021, 70: 1-11..

1. Costa E L V, Gonzalez Lima R, Amato M B P. Electrical impedance tomography. Curr Opin Crit Care, 2009, 15(1): 18-24..
2. Song Zhenzhong, Wen Jianming, Wan Nen, et al. An optimization algorithm H-GVSPM for electrical impedance tomography. IEEE Sens J, 2023, 23(5): 4518-4526..
3. 王子辰, 付荣, 张新宇, 等. 基于Deep CG的肺部电阻抗成像方法. 中国生物医学工程学报, 2023, 42(2): 148-157..
4. 叶明, 李晓丞, 刘凯, 等. 一种基于U～2-Net模型的电阻抗成像方法. 仪器仪表学报, 2021, 42(2): 235-243..
5. 孙敏, 贾辉, 秦卿雯, 等. 电阻抗成像原位在线监测超滤膜污染行为研究. 化工学报, 2022, 73(4): 1754-1762, 1847..
6. Gallo G J, Thostenson E T. Spatial damage detection in electrically anisotropic fiber-reinforced composites using carbon nanotube networks. Compos Struct, 2016, 141: 14-23..
7. Bao Huilu, Li Jianping, Wen Jianming, et al. Quantitative evaluation of burn injuries based on electrical impedance spectroscopy of blood with a seven-parameter equivalent circuit. Sensors, 2021, 21(4): 1496..
8. 陈晓艳, 吴君, 王化祥, 等. 不同体位呼吸过程的电阻抗断层成像研究. 生物医学工程学杂志, 2010, 27(5): 1004-1007..
9. 王慧泉, 尹剑利, 李光旭, 等. 磁探测电阻抗成像呼吸监测仿真研究. 生物医学工程学杂志, 2017, 34(1): 27-33..
10. 张静, 刘晓姝, 滕鸿, 等. 应用电阻抗断层成像技术评价神经调节辅助通气和压力支持通气模式对慢性阻塞性肺疾病急性加重患者的影响. 上海医学, 2020, 43(5): 299-301..
11. Sella N, Pettenuzzo T, Zarantonello F, et al. Electrical impedance tomography: a compass for the safe route to optimal PEEP. Resp Med, 2021, 187(11): 106555..
12. Shono A, Kotani T, Inéz Frerichs. Personalisation of therapies in COVID-19 associated acute respiratory distress syndrome, using electrical impedance tomography. J Crit Care Med (Targu Mures), 2021, 7(1): 62-66..
13. Gibot S, Conrad M, Courte G, et al. Positive end-expiratory pressure setting in COVID-19-related acute respiratory distress syndrome: comparison between electrical impedance tomography, PEEP/FiO2 tables, and transpulmonary pressure. Front Med (Lausanne), 2021, 8: 720920..
14. Zamani M, Kallio M, Bayford R, et al. Generation of anatomically inspired Human Airway Tree using electrical impedance tomography: a method to estimate regional lung filling characteristics. IEEE T Med Imaging, 2022, 41(5): 1125-1137..
15. 李芳, 方海东, 吴阳, 等. 面向肺功能检测的便携式电阻抗成像系统开发. 机械制造与自动化, 2022, 51(5): 239-242, 246..
16. 李星, 杨帆, 余晓, 等. 基于自诊断正则化的电阻抗成像逆问题研究. 生物医学工程学杂志, 2018, 35(3): 460-467..
17. 曹璐, 杨滨, 李昊庭, 等. 一种基于频谱约束的多频动态电阻抗断层成像算法. 生物医学工程学杂志, 2020, 37(1): 80-86..
18. 丁明亮, 李晓童, 卢立晖. 基于邻域信息和快速FCM的肺部电阻抗成像伪迹优化算法. 电子与信息学报, 2022, 44(9): 3320-3327..
19. Wu Yang, Chen Bai, Liu Kai, et al. Bayesian image reconstruction using weighted Laplace prior for lung respiratory monitoring with electrical impedance tomography. IEEE T Instrum Meas, 2023, 72: 1-11..
20. Wu Yang, Chen Bai, Liu Kai, et al. Shape reconstruction with multiphase conductivity for electrical impedance tomography using improved convolutional neural network method. IEEE Sens J, 2021, 21(7): 9277-9287..
21. Zhu L, Lu W, Soleimani M, et al. Electrical impedance tomography guided by digital twins and deep learning for lung monitoring. IEEE T Instrum Meas, 2023, 72: 1-9..
22. Wang Peng, Xie Lili, Sun Yicai. Electrical impedance tomography based on BP neural network and improved PSO// 2009 International Conference on Machine Learning and Cybernetics. Hebei: IEEE, 2009: 1059-1064..
23. 戎舟, 李若愚, 方滔. 基于多层人工神经网络的电阻抗成像算法. 国外电子测量技术, 2021, 40(1): 80-86..
24. 郑皓楠. 电阻抗成像激励测量方式和成像方法研究. 电子测试, 2021(5): 43-45..
25. 蔡荣辉, 崔雨轩, 薛培静. 三层BP神经网络隐层节点数确定方法探究. 电脑与信息技术, 2017, 25(5): 29-33..
26. 巴斌, 刘国春, 李韬, 等. 基于哈达玛积扩展子空间的到达时间和波达方向联合估计. 物理学报, 2015, 64(7): 384-392..
27. Xu Yangyang, Li Yaotao, Li Zhengbo. Some results on the Hadamard product of tensors. Bull Iran Math Soc, 2019, 45(4): 1193-1219..
28. Dong G Y, Endo H, Hayano S, et al. GVSPM for reconstruction in electrical impedance tomography. IEEE T Magn, 2003, 39(3): 1630-1633..
29. Takei M, Saito Y. Application of the generalized vector sampled pattern matching method to reconstruction of electrical capacitance CT images. Meas Sci Technol, 2004, 15(7): 1371-1381..
30. Xu Zhen, Huang Junchao, Jiang Yandan, et al. An image reconstruction algorithm for a 12-electrode capacitively coupled electrical resistance tomography system under 2-electrode excitation strategy. IEEE T Instrum Meas, 2021, 70: 1-11..

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