Vol. 169
Latest Volume
All Volumes
PIER 179 [2024] PIER 178 [2023] PIER 177 [2023] PIER 176 [2023] PIER 175 [2022] PIER 174 [2022] PIER 173 [2022] PIER 172 [2021] PIER 171 [2021] PIER 170 [2021] PIER 169 [2020] PIER 168 [2020] PIER 167 [2020] PIER 166 [2019] PIER 165 [2019] PIER 164 [2019] PIER 163 [2018] PIER 162 [2018] PIER 161 [2018] PIER 160 [2017] PIER 159 [2017] PIER 158 [2017] PIER 157 [2016] PIER 156 [2016] PIER 155 [2016] PIER 154 [2015] PIER 153 [2015] PIER 152 [2015] PIER 151 [2015] PIER 150 [2015] PIER 149 [2014] PIER 148 [2014] PIER 147 [2014] PIER 146 [2014] PIER 145 [2014] PIER 144 [2014] PIER 143 [2013] PIER 142 [2013] PIER 141 [2013] PIER 140 [2013] PIER 139 [2013] PIER 138 [2013] PIER 137 [2013] PIER 136 [2013] PIER 135 [2013] PIER 134 [2013] PIER 133 [2013] PIER 132 [2012] PIER 131 [2012] PIER 130 [2012] PIER 129 [2012] PIER 128 [2012] PIER 127 [2012] PIER 126 [2012] PIER 125 [2012] PIER 124 [2012] PIER 123 [2012] PIER 122 [2012] PIER 121 [2011] PIER 120 [2011] PIER 119 [2011] PIER 118 [2011] PIER 117 [2011] PIER 116 [2011] PIER 115 [2011] PIER 114 [2011] PIER 113 [2011] PIER 112 [2011] PIER 111 [2011] PIER 110 [2010] PIER 109 [2010] PIER 108 [2010] PIER 107 [2010] PIER 106 [2010] PIER 105 [2010] PIER 104 [2010] PIER 103 [2010] PIER 102 [2010] PIER 101 [2010] PIER 100 [2010] PIER 99 [2009] PIER 98 [2009] PIER 97 [2009] PIER 96 [2009] PIER 95 [2009] PIER 94 [2009] PIER 93 [2009] PIER 92 [2009] PIER 91 [2009] PIER 90 [2009] PIER 89 [2009] PIER 88 [2008] PIER 87 [2008] PIER 86 [2008] PIER 85 [2008] PIER 84 [2008] PIER 83 [2008] PIER 82 [2008] PIER 81 [2008] PIER 80 [2008] PIER 79 [2008] PIER 78 [2008] PIER 77 [2007] PIER 76 [2007] PIER 75 [2007] PIER 74 [2007] PIER 73 [2007] PIER 72 [2007] PIER 71 [2007] PIER 70 [2007] PIER 69 [2007] PIER 68 [2007] PIER 67 [2007] PIER 66 [2006] PIER 65 [2006] PIER 64 [2006] PIER 63 [2006] PIER 62 [2006] PIER 61 [2006] PIER 60 [2006] PIER 59 [2006] PIER 58 [2006] PIER 57 [2006] PIER 56 [2006] PIER 55 [2005] PIER 54 [2005] PIER 53 [2005] PIER 52 [2005] PIER 51 [2005] PIER 50 [2005] PIER 49 [2004] PIER 48 [2004] PIER 47 [2004] PIER 46 [2004] PIER 45 [2004] PIER 44 [2004] PIER 43 [2003] PIER 42 [2003] PIER 41 [2003] PIER 40 [2003] PIER 39 [2003] PIER 38 [2002] PIER 37 [2002] PIER 36 [2002] PIER 35 [2002] PIER 34 [2001] PIER 33 [2001] PIER 32 [2001] PIER 31 [2001] PIER 30 [2001] PIER 29 [2000] PIER 28 [2000] PIER 27 [2000] PIER 26 [2000] PIER 25 [2000] PIER 24 [1999] PIER 23 [1999] PIER 22 [1999] PIER 21 [1999] PIER 20 [1998] PIER 19 [1998] PIER 18 [1998] PIER 17 [1997] PIER 16 [1997] PIER 15 [1997] PIER 14 [1996] PIER 13 [1996] PIER 12 [1996] PIER 11 [1995] PIER 10 [1995] PIER 09 [1994] PIER 08 [1994] PIER 07 [1993] PIER 06 [1992] PIER 05 [1991] PIER 04 [1991] PIER 03 [1990] PIER 02 [1990] PIER 01 [1989]
2020-12-30
A Review of Algorithms and Hardware Implementations in Electrical Impedance Tomography (Invited)
By
Progress In Electromagnetics Research, Vol. 169, 59-71, 2020
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.
Citation
Zheng Zong, Yusong Wang, and Zhun Wei, "A Review of Algorithms and Hardware Implementations in Electrical Impedance Tomography (Invited)," Progress In Electromagnetics Research, Vol. 169, 59-71, 2020.
doi:10.2528/PIER20120401
References

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.