Biological tissue detection based on electrical impedance spectroscopic tomograsphy

Yin Hong-Run; Ye Ming; Wu Yang; Liu Kai; Pan Hua-Ping; Yao Jia-Feng

doi:10.7498/aps.71.20211600

Abstract

A bioimpedance spectroscopic imaging method for detecting the biological tissue based on electrical impedance tomography (EIT) and bioimpedance spectroscopy (BIS) is proposed. This method visualizes the target area and accurately recognizes the target type, which can be used for detecting the early lung cancer, assist clinicians in accurately detecting the early lung cancer, and improving the cure rate of early lung cancer. In this paper the bioimpedance spectroscopic imaging method is verified to be feasible and effective in detecting the early lung cancer through numerical simulation. The simulation results show that 1) the bioimpedance spectroscopic imaging method can realize the visualization of the early lung cancer area and accurately distinguish the type of early lung cancer, and 2) the optimal number of acquisitions of impedance spectroscopy is 4, and the best classifier is Linear-SVM, and the average classification accuracy of 5-fold cross-validation can reach 99.9%. In order to verify the simulation results, three biological tissues with different electrical characteristics are selected to simulate cancerous regions used for detection. The experimental results show that the method can visualize the biological tissue area and distinguish the type of biological tissue. This method can integrate the advantages of electrical impedance imaging and bioimpedance spectroscopy, and is very promising way of detecting early lung cancer.

Keywords:

Authors and contacts

1.
College of Electrical and Mechanical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2.
Affiliated Jiangning Hospital, Nanjing Medical University, Nanjing 211100, China
3.
Engineering Medicine Joint Laboratory, Nanjing Jiangning Hospital, Nanjing 211100, China

Corresponding author: Ye Ming, yeming5@nuaa.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant No. 62071224), the Key R&D Program of Jiangsu Province (Social Development), China (Grant No. BE2021618), the Open Fund of State Key Laboratory of Precision Measuring Technology and Instruments, China (Grant No. pilab2107), the Clinical Medicine Science and Technology Development Fund Project of Jiangsu University, China (Grant NO. JLY2021156), and the Science and Technology Development Fund of Nanjing Medical University, China (Grant No. NMUB2020164).

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References

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[13]	姚佳烽, 胡松佩, 杨璐, 吴阳, 韩伟, 刘凯 2021 70 158704 Google Scholar Yao J, Hu S, Yang L, Wu Y, Han W, Liu K 2021 Acta Phys. Sin. 70 158704 Google Scholar
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图 1 BIST肺癌检测原理图

Figure 1. Principles of BIST Lung Cancer Detection.

DownLoad: Full-Size Img PowerPoint

图 2 实验设备

Figure 2. Experimental set-up.

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图 3 BIST仿真结果　(a) 肺癌Nyquist图; (b)模式识别分类准确率

Figure 3. BIST simulation results: (a) Nyquist diagram of lung cancer; (b) pattern recognition classification accuracy.

DownLoad: Full-Size Img PowerPoint

图 4 BIST实验结果

Figure 4. BIST experiment results.

DownLoad: Full-Size Img PowerPoint

表 1 相关生物组织电学参数

Table 1. Electrical parameters of the related biological tissue.

频率f/kHz		1	10	200	400	4000
心脏	电导率 σ/(S·m^–1)	0.1036	0.1542	0.2382	0.2685	0.4324
心脏	相对介电常数ε/10³	352.9	70.05	6.001	3.788	0.594
肺	电导率 σ/(S·m^–1)	0.2157	0.2429	0.2835	0.3000	0.3983
肺	相对介电常数 ε/10³	252.1	34.04	3.280	2.161	0.3613
其它	电导率 σ/(S·m^–1)	0.2216	0.2352	0.2642	0.2935	0.3960
其它	相对介电常数 ε/10³	298.0	17.63	4.271	2.906	0.2630

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[1]	Forman D, Ferlay J, Jemal A, Bray F, Ward E, Center M 2015 CA-Cancer J. Clin. 65 87 Google Scholar
[2]	Ferlay J, Colombet M, Soerjomataram I, et al. 2018 Eur. J. Cancer 103 356 Google Scholar
[3]	Chen W Q, Zheng R S, Baade P D, Zhang S W, Yu X Q 2016 CA-CANCER J CLIN 66 115 Google Scholar
[4]	Blankman P, Hasan D, Mourik M, Gommers D 2013 Intens. Care Med. 39 1057 Google Scholar
[5]	Rhee C K, Chau N Q, Yunus F, Matsunaga K, Perng D 2019 Respirology 24 1018 Google Scholar
[6]	Hao Z H, Cui Z Q, Yue S H, Wang H X 2018 Rev. Sci. Instrum. 89 064702 Google Scholar
[7]	Victorino J, Borges J, Okamoto V, Matos G, Tucci M, Caramez M, Tanaka H 2004 Am. J. Resp. Crit. Care 169 791 Google Scholar
[8]	Sun B, Yue S, Hao Z, et al. 2019 IEEE Sens. J. 19 3049 Google Scholar
[9]	Gao J, Yue S, Chen J, Wang H 2014 Bio-med. Mater. Eng. 24 2229 Google Scholar
[10]	叶明, 李晓丞, 刘凯, 韩伟, 姚佳烽 2021 仪器仪表学报 42 235 Google Scholar Ye M, Li X C, Liu K, Han W, Yao J F 2021 Chin. J. Sci. Instrum. 42 235 Google Scholar
[11]	Wu Y, Chen B, Liu K, Zhu C J, Pan H P, Jia J B, Wu H T, Yao J F 2021 IEEE Sens. J. 21 9277 Google Scholar
[12]	Wang L, Hu S, Liu K, Chen B, Wu H, Jia J, Yao J 2020 Rev. Sci. Instrum. 91 124104 Google Scholar
[13]	姚佳烽, 胡松佩, 杨璐, 吴阳, 韩伟, 刘凯 2021 70 158704 Google Scholar Yao J, Hu S, Yang L, Wu Y, Han W, Liu K 2021 Acta Phys. Sin. 70 158704 Google Scholar
[14]	Lu L, Hamzaoui L, Brown B H, Rigaud B, Smallwood R, Barber D, Morucci J 1996 Med. Biol. Eng. Comput. 34 122 Google Scholar
[15]	Mahdavi R, Hosseinpour P, Abbasvandi F, Mehrvarz S, Abdolahad M 2020 Biosens. Bioelectron. 165 112421 Google Scholar
[16]	Wang Y R, Yue S H 2018 Ieee 13th World Congress on Intelligent Control and Automation (WCICA) Changsha, PEOPLESR CHINA, July 04–08, 2018 pp341–346
[17]	陈晓艳, 赵秋红 2014 天津科技大学学报 29 50 Google Scholar Chen X, Zhao Q 2014 J. Tianjin Univ. Sci. Tech. 29 50 Google Scholar
[18]	Gabriel C, Gabriel S, Corthout E 1996 Phys. Med. Biol. 41 2231 Google Scholar
[19]	Guardo R, Boulay C 1991 IEEE Trans. Bio-med. Eng. 38 617 Google Scholar
[20]	姚佳烽, 万建芬, 杨璐, 刘凯, 陈柏, 吴洪涛 2020 69 163301 Google Scholar Yao J F, Wan J F, Yang L, Liu K, Chen B, Wu H T 2020 Acta Phys. Sin. 69 163301 Google Scholar
[21]	Manavalan B, Shin T H, Lee G 2018 Front. Microbiol. 9 476 Google Scholar
[22]	Chan C W, Paelinckx D 2008 Remote Sens. Environ. 112 2999 Google Scholar
[23]	Fan M, Zheng B, Li L 2015 J. Bioinf. Comput. Biol. 13 1550022 Google Scholar

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