volume | PIER Journals

Abstract

In recent years, electrical impedance tomography (EIT) has attracted intensive interests due to its noninvasive, ionizing radiation-free, and low-cost advantages, which is promising for both biomedical imaging and industry nondestructive tests. The purpose of this paper is to review state-of-the-art methods including both algorithms and hardwares in EIT. More specifically, for the advanced reconstruction algorithms in mainstream, we offer some insights on classification and comparison. As for the measurement equipment, the structure, configuration modes, and typical systems are reviewed. Furthermore, we discuss the limitations and challenges in EIT technique, such as low-spatial resolution and nonlinear-inversion problems, where future directions, such as solving EIT problems with deep learning, have also been addressed.

1. Williams, R. A. and M. S. Beck, "Chapter 1 — Introduction to process tomography," Process Tomography, Vol. 13, No. 2, 3-12, 1995.
doi:10.1016/B978-0-08-093801-1.50005-8

2. Yang, L., C. Zhang, W. Liu, H. Wang, J. Xia, B. Liu, X. Shi, X. Dong, F. Fu, M. Dai, and J. L. Campos, "Real-time detection of hemothorax and monitoring its progression in a piglet model by electrical impedance tomography: A feasibility study," BioMed Research International, Vol. 2020, Article ID 1357160, 2020.

3. Schullcke, B., B. Gong, S. Krueger-Ziolek, et al. "Structural-functional lung imaging using a combined CT-EIT and a discrete cosine transformation reconstruction method," Scientific Reports, Vol. 6, 25951, 2016.
doi:10.1038/srep25951

4. Hong, S., J. Lee, J. Bae, et al. "A 10.4mW electrical impedance tomography SoC for portable real-time lung ventilation monitoring system," IEEE Journal of Solid-State Circuits, Vol. 50, No. 11, 2501-2512, 2015.
doi:10.1109/JSSC.2015.2464705

5. Boverman, G., T. J. Kao, X. Wang, et al. "Detection of small bleeds in the brain with electrical impedance tomography," Physiol. Meas., Vol. 37, No. 6, 727-750, 2016.
doi:10.1088/0967-3334/37/6/727

6. Murphy, E. K., A. Mahara, and R. J. Halter, "Absolute reconstructions using rotational electrical impedance tomography for breast cancer imaging," IEEE Transactions on Medical Imaging, Vol. 36, No. 4, 892-903, 2017.
doi:10.1109/TMI.2016.2640944

7. Sarode, V., S. S. Patkar, and A. N. Cheeran, "Comparison of factors affecting the detection of small impurities in breast cancer using EIT," International Journal of Engineering Science & Technology, Vol. 5, No. 6, 1267-1271, 2013.

8. Podczeck, F., C. L. Mitchell, J. M. Newton, et al. "The gastric emptying of food as measured by gamma-scintigraphy and electrical impedance tomography (EIT) and its influence on the gastric emptying of tablets of different dimensions," Journal of Pharmacy & Pharmacology, Vol. 59, No. 11, 1527-1536, 2010.
doi:10.1211/jpp.59.11.0010

9. Tomasino, S., R. Sassanelli, C. Marescalco, et al. "Electrical impedance tomography and prone position during ventilation in COVID-19 Pneumonia: Case reports and a brief literature review," Semin. Cardiothorac. Vasc. Anesth., Vol. 24, No. 4, 287-292, 2020.
doi:10.1177/1089253220958912

10. Dickin, F. and M. Wang, "Electrical resistance tomography for process applications," Measurement Science and Technology, Vol. 7, No. 3, 247, 1996.
doi:10.1088/0957-0233/7/3/005

11. Tapp, H. S., A. J. Peyton, E. K. Kemsley, et al. "Chemical engineering applications of electrical process tomography," Sensors & Actuators B: Chemical, Vol. 92, No. 1/2, 17-24, 2003.
doi:10.1016/S0925-4005(03)00126-6

12. Kruger, M. V. P., Tomography as a metrology technique for semiconductor manufacturing, Ph.D. Thesis, University of California, Berkeley, 2003.

13. Linderholm, P., L. Marescot, M. H. Loke, et al. "Cell culture imaging using microimpedance tomography," IEEE Transactions on Biomedical Engineering, Vol. 55, No. 1, 138, 2008.
doi:10.1109/TBME.2007.910649

14. Sun, T., S. Tsuda, K. P. Zauner, et al. "On-chip electrical impedance tomography for imaging biological cells," Biosensors & Bioelectronics, Vol. 25, No. 5, 1109-1115, 2010.
doi:10.1016/j.bios.2009.09.036

15. Hou, T. C., K. J. Loh, and J. P. Lynch, "Electrical impedance tomography of carbon nanotube composite materials," Proceedings of SPIE — The International Society for Optical Engineering, 2007.

16. Hou, T. C., K. J. Loh, and J. P. Lynch, "Spatial conductivity mapping of carbon nanotube composite thin films by electrical impedance tomography for sensing applications," Nanotechnology, Vol. 18, No. 31, 962-969, 2007.
doi:10.1088/0957-4484/18/31/315501

17. Liu, K., Y. Wu, S. Wang, et al. "Artificial sensitive skin for robotics based on electrical impedance tomography," Advanced Intelligent Systems, 1-13, 2020.

18. Jiang, D., Y. Wu, and A. Demosthenous, "Hand gesture recognition using three-dimensional electrical impedance tomography," IEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 67, No. 9, 1554-1558, 2020.
doi:10.1109/TCSII.2020.3006430

19. Wang, Z., S. Yue, H. Wang, et al. "Data preprocessing methods for electrical impedance tomography: A review," Physiological Measurement, Vol. 41, No. 9, 09TR02, 2020.
doi:10.1088/1361-6579/abb142

20. Wei, Z., D. Liu, and X. Chen, "Dominant-current deep learning scheme for electrical impedance tomography," IEEE Transactions on Biomedical Engineering, Vol. 66, No. 9, 2546-2555, 2019.
doi:10.1109/TBME.2019.2891676

21. Liu, D., V. Kolehmainen, et al. "Nonlinear difference imaging approach to three-dimensional electrical impedance tomography in the presence of geometric modeling errors," IEEE Transactions on Biomedical Engineering, Vol. 63, No. 9, 1956-1965, 2016.
doi:10.1109/TBME.2015.2509508

22. Chitturi, V. and F. Nagi, "Spatial resolution in electrical impedance tomography: A topical review," Journal of Electrical Bioimpedance, Vol. 8, No. 1, 66, 2017.
doi:10.5617/jeb.3350

23. Zhang, K., M. Li, et al. "Three-dimensional electrical impedance tomography with multiplicative regularization," IEEE Transactions on Biomedical Engineering, Vol. 13, No. 6, 1139-1159, 2019.

24. Smyl, D. and D. Liu, "Optimizing electrode positions in 2D Electrical Impedance Tomography using deep learning," IEEE Transactions on Instrumentation and Measurement, 2020.

25. Agnelli, J. P., A. Col, M. Lassas, et al. "Classification of stroke using neural networks in electrical impedance tomography," Inverse Problems, Vol. 36, No. 11, 115008, 2020.
doi:10.1088/1361-6420/abbdcd

26. Borcea, L., "Topical review: Electrical impedance tomography," Inverse Problems, Vol. 18, No. 6, R99, 2002.
doi:10.1088/0266-5611/18/6/201

27. Padilha Leitzke, J. and H. Zangl, "A review on electrical impedance tomography spectroscopy," Sensors, Vol. 20, No. 18, 2020.
doi:10.3390/s20185160

28. Schwan, H. P., "Electrical properties of tissues and cell suspensions: Mechanisms and models," International Conference of the IEEE Engineering in Medicine & Biology Society, IEEE, 1994.

29. Somersalo, E., M. Cheney, and D. Isaacson, "Existence and uniqueness for electrode models for electric current computed tomography," SIAM Journal on Applied Mathematics, Vol. 52, No. 4, 1023-1040, 1992.
doi:10.1137/0152060

30. Jackson, J., "Classical Electrodynamics," Wiley, 1998.

31. Cheng, K. S. and D. Isaacson, "Electrode models for electric current computed tomography," IEEE Transactions on Biomedical Engineering, Vol. 36, No. 9, 918-924, 1989.
doi:10.1109/10.35300

32. Xiang, J., Y. Dong, and Y. Yang, "Multi-frequency electromagnetic tomography for acute stroke detection using frequency constrained sparse bayesian learning," IEEE Transactions on Medical Imaging, Vol. 39, No. 12, 4102-4112, 2020.
doi:10.1109/TMI.2020.3013100

33. Liu, S., Y. Huang, H. Wu, et al. "Efficient multi-task structure-aware sparse bayesian learning for frequency-difference electrical impedance tomography," IEEE Transactions on Industrial Informatics, Vol. 17, No. 1, 463-472, 2021.
doi:10.1109/TII.2020.2965202

34. Liu, S., J. Jia, Y. D. Zhang, et al. "Image reconstruction in electrical impedance tomography based on structure-aware sparse Bayesian learning," IEEE Transactions on Medical Imaging, Vol. 37, No. 9, 2090-2102, 2018.
doi:10.1109/TMI.2018.2816739

35. Darma, P. N., M. R. Baidillah, M. W. Sifuna, et al. "Real-time dynamic imaging method for flexible boundary sensor in wearable electrical impedance tomography," IEEE Sensors Journal, Vol. 20, No. 16, 9469-9479, 2020.

36. Wei, Z. and X. Chen, "Induced-current learning method for nonlinear reconstructions in electrical impedance tomography," IEEE Transactions on Medical Imaging, Vol. 39, No. 5, 1326-1334, 2019.
doi:10.1109/TMI.2019.2948909

37. Wei, Z., R. Chen, H. Zhao, and X. Chen, "Two FFT subspace-based optimization methods for electrical impedance tomography," Progress In Electromagnetics Research, Vol. 157, 111-120, 2016.
doi:10.2528/PIER16082302

38. Lucas, A., M. Iliadis, R. Molina, et al. "Using deep neural networks for inverse problems in imaging: Beyond analytical methods," IEEE Signal Processing Magazine, Vol. 35, No. 1, 20-36, 2018.
doi:10.1109/MSP.2017.2760358

39. Chen, X., Z. Wei, M. Li, and P. Rocca, "A review of deep learning approaches for inverse scattering problems (invited review)," Progress In Electromagnetics Research, Vol. 167, 67-81, 2020.
doi:10.2528/PIER20030705

40. Mccann, M. T., K. H. Jin, and M. Unser, "Convolutional neural networks for inverse problems in imaging: A review," IEEE Signal Processing Magazine, Vol. 34, No. 6, 85-95, 2017.
doi:10.1109/MSP.2017.2739299

41. Fan, Y. and L. Ying, "Solving electrical impedance tomography with deep learning," Journal of Computational Physics, Vol. 404, 109119, 2019.

42. Xia, Z., Z. Cui, Y. Chen, et al. "Generative adversarial networks for dual-modality electrical tomography in multi-phase flow measurement," Measurement, 2020.

43. Kosowski, G. and T. Rymarczyk, "Using neural networks and deep learning algorithms in electrical impedance tomography," Informatyka Automatyka Pomiary w Gospodarce i Ochronie Srodowiska, Vol. 7, No. 3, 99-102, 2017.
doi:10.5604/01.3001.0010.5226

44. Hamilton, S. J. and A. Hauptmann, "Deep D-bar: Real time electrical impedance tomography imaging with deep neural networks," IEEE Transactions on Medical Imaging, Vol. 37, No. 10, 2367-2377, 2017.
doi:10.1109/TMI.2018.2828303

45. Ren, S., K. Sun, C. Tan, et al. "A two-stage deep learning method for robust shape reconstruction with electrical impedance tomography," IEEE Transactions on Instrumentation and Measurement, Vol. 69, No. 7, 4887-4897, 2019.
doi:10.1109/TIM.2019.2954722

46. Khan, T. A. and S. H. Ling, "Review on electrical impedance tomography: Artificial intelligence methods and its applications," Algorithms, Vol. 12, No. 5, 88, 2019.
doi:10.3390/a12050088

47. Liu, D., D. Gu, D. Smyl, et al. "Shape reconstruction using boolean operations in electrical impedance tomography," IEEE Transactions on Medical Imaging, Vol. 39, No. 9, 2954-2964, 2020.
doi:10.1109/TMI.2020.2983055

48. Huska, M., D. Lazzaro, S. Morigi, et al. "Spatially-adaptive variational reconstructions for linear inverse electrical impedance tomography," Journal of Scientific Computing, Vol. 84, No. 3, 2020.
doi:10.1007/s10915-020-01295-w

49. Hamilton, S. J., J. L. Mueller, and T. R. Santos, "Robust computation in 2D absolute EIT (a-EIT) using D-bar methods with the ‘exp’ approximation," Physiological Measurement, Vol. 39, No. 6, 064005, 2018.
doi:10.1088/1361-6579/aac8b1

50. Chaulet, N., S. Arridge, T. Betcke, et al. "The factorization method for three dimensional electrical impedance tomography," Mathematics, Vol. 30, No. 4, 45005-45019(15), 2014.

51. Vauhkonen, M. and D. Vadasz, "Tikhonov regularization and prior information in electrical impedance tomography," IEEE Transactions on Medical Imaging, Vol. 17, No. 2, 285-293, 1998.
doi:10.1109/42.700740

52. Gonzalez, G., J. M. J. Huttunen, V. Kolehmainen, et al. "Experimental evaluation of 3D electrical impedance tomography with total variation prior," Inverse Problems in Science & Engineering, Vol. 2015, 1-21, 2015.

53. Gehre, M., T. Kluth, A. Lipponen, et al. "Sparsity reconstruction in electrical impedance tomography: An experimental evaluation," Journal of Computational and Applied Mathematics, Vol. 236, No. 8, 2126-2136, 2012.
doi:10.1016/j.cam.2011.09.035

54. Cherkaev, A. V. and L. V. Gibiansky, "Variational principles for complex conductivity, viscoelasticity, and similar problems in media with complex moduli," Journal of Mathematical Physics, Vol. 35, No. 1, 127-145, 1994.
doi:10.1063/1.530782

55. Li, K., N. Yang, J. Wang, et al. "Size projection algorithm: Optimal thresholding value selection for image segmentation of electrical impedance tomography," Mathematical Problems in Engineering, Vol. 2019, No. 6, 1-11, 2019.

56. Li, M., K. Zhang, R. Guo, F. Yang, S. Xu, and A. Abubakar, "Supervised descent method for electrical impedance tomography," 2019 Photonics & Electromagnetics Research Symposium — Fall (PIERS — Fall), 2342-2348, Xiamen, China, December 17–20, 2019.

57. Hu, D., K. Lu, and Y. Yang, "Image reconstruction for electrical impedance tomography based on spatial invariant feature maps and convolutional neural network," 2019 IEEE International Conference on Imaging Systems and Techniques (IST), IEEE, 2019.

58. Wei, Z. and X. Chen, "Deep-learning schemes for full-wave nonlinear inverse scattering problems," IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 4, 1849-1860, 2018.
doi:10.1109/TGRS.2018.2869221

59. Wei, Z. and X. Chen, "Physics-inspired convolutional neural network for solving full-wave inverse scattering problems," IEEE Transactions on Antennas and Propagation, Vol. 67, No. 9, 6138-6148, 2019.
doi:10.1109/TAP.2019.2922779

60. Bera, T. K. and J. Nagaraju, "Studying the resistivity imaging of chicken tissue phantoms with different current patterns in Electrical Impedance Tomography (EIT)," Measurement, Vol. 45, No. 4, 663-682, 2012.
doi:10.1016/j.measurement.2012.01.002

61. Jones, D. M., R. H. Smallwood, D. R. Hose, et al. "Constraints on tetrapolar tissue impedance measurements," Electronics Letters, Vol. 37, No. 25, 1515-1517, 2002.
doi:10.1049/el:20011034

62. Chandra, H., S. W. Allen, S. W. Oberloier, et al. "Open-source automated mapping four-point probe," Materials, Vol. 10, No. 2, 110, 2017.
doi:10.3390/ma10020110

63. Tan, C., S. Liu, J. Jia, et al. "A wideband electrical impedance tomography system based on sensitive bioimpedance spectrum bandwidth," IEEE Transactions on Instrumentation and Measurement, Vol. 2019, 1-11, 2019.

64. Yue, X. and C. Mcleod, "FPGA design and implementation for EIT data acquisition," Physiological Measurement, Vol. 29, No. 10, 1233-1233, 2008.
doi:10.1088/0967-3334/29/10/007

65. Huang, S. K. and K. J. Loh, "Development of a portable electrical impedance tomography data acquisition system for near-real-time spatial sensing," SPIE Proceedings, Vol. 9435, 11 pages, 2015.

66. Kourunen, J., T. Savolainen, A. Lehikoinen, et al. "Suitability of a PXI platform for an electrical impedance tomography system," Measurement Science & Technology, Vol. 20, No. 1, 015503, 2012.
doi:10.1088/0957-0233/20/1/015503

67. Xu, Z., J. Yao, Z. Wang, et al. "Development of a portable electrical impedance tomography system for biomedical applications," IEEE Sensors Journal, Vol. 18, 8117-8124, 2018.
doi:10.1109/JSEN.2018.2864539

68. Huang, J. J., Y. H. Hung, J. J. Wang, et al. "Design of wearable and wireless electrical impedance tomography system," Measurement, Vol. 78, 9-17, 2016.
doi:10.1016/j.measurement.2015.09.031

69. Rymarczyk, T., Tomographic Imaging in Environmental, Industrial and Medical Applications: Tomography, Internet of Things, Machine Learning, Distributed Systems, Big Data, Industry 4.0, Innovation Press Publishing House, University of Economics and Innovation, 2019.

70. Rymarczyk, T., S. Filipowicz, and J. Sikora, "Comparing methods of image reconstruction in electrical impedance tomography," Computer Applications in Electrical Engineering, 2011.

71. Rymarczyk, T., "Minimization of objective function in electrical impedance tomography by topological derivative," Przeglad Elektrotechniczny, Vol. 1, No. 6, 139-142, 2019.
doi:10.15199/48.2019.06.25

72. Wei, Z. and X. Chen, "Uncertainty quantification in inverse scattering problems with bayesian convolutional neural networks," IEEE Transactions on Antennas and Propagation, IEEE, 2020.

Dr. Zheng Zong

Dr. Yusong Wang

Prof. Zhun Wei