Survey of EIT Image Reconstruction Algorithms

doi:10.1007/s12204-021-2333-1

Abstract

Abstract: With the recent promotion of clinical applications of electrical impedance tomography (EIT) technology,more scholars have begun studying EIT technology. Although the principle of EIT technology seems simple,EIT image reconstruction is a non-linear and ill-posed problem that is quite difficult to solve because of its soft field characteristics and the inhomogeneous distribution of its sensitive field. What’s more, the EIT reconstruction algorithm requires further improvements in robustness, clarity, etc. The image-reconstruction algorithm and image quality are among the key challenges in the application of EIT technology; thus, more research is urgently needed to improve the performance of EIT technology and use it to solve a larger variety of clinical problems. In this paper, we pay special attention to the latest advances in the study of EIT image-reconstruction algorithms to provide a convenient reference for EIT beginners and researchers who are newly involved in research on EIT image reconstruction.

Key words: electrical impedance tomography(EIT)| image reconstruction| inverse problem

CLC Number:

TP 237

ZHANG Mingzhu(张明珠), MA Yixin* (马艺馨), HUANG Ningning (黄宁宁), GE Hao (葛浩). Survey of EIT Image Reconstruction Algorithms[J]. J Shanghai Jiaotong Univ Sci, 2022, 27(2): 211-218.

References 44

[35]	HYV¨ONEN N, MUSTONEN L. Generalized linearization techniques in electrical impedance tomography [J].Numerische Mathematik, 2018, 140(1): 95-120.
[1]	KUEN J, WOO E, SEO J. Multi-frequency timedifference complex conductivity imaging of canine and
[36]	CHUNG E T, CHAN T F, TAI X C. Electrical impedance tomography using level set representation and total variational regularization [J]. Journal of Computational Physics, 2005, 205(1): 357-372.
	human lungs using the KHU Mark1 EIT system [J].Physiological Measurement, 2009, 30(6): S149-S164.
[37]	LIU D, SMYL D, DU J F. A parametric level set-based approach to difference imaging in electrical impedance tomography [J]. IEEE Transactions on Medical Imaging,2019, 38(1): 145-155.
[2]	TAWIL D S, RYE D, VELONAKI M. Improved image reconstruction for an EIT-based sensitive skin with multiple internal electrodes [J]. IEEE Transactions on Robotics, 2011, 27(3): 425-435.
[38]	LIU D, KHAMBAMPATI A K, DU J F. A parametric level set method for electrical impedance tomography [J]. IEEE Transactions on Medical Imaging, 2018,37(2): 451-460.
[3]	YANG Y, JIA J, SMITH S, et al. A miniature electrical impedance tomography sensor and 3-D image reconstruction for cell imaging [J]. IEEE Sensors Journal,2017, 17(2): 514-523.
[39]	LIU D, GU D, SMYL D, et al. B-spline-based sharp feature preserving shape reconstruction approach for electrical impedance tomography [J]. IEEE Transactions on Medical Imaging, 2019, 38(11): 2533-2544.
[4]	PILLOW J J, FRERICHS I, STOCKS J. Lung function tests in neonates and infants with chronic lung disease: Global and regional ventilation inhomogeneity [J]. Pediatric Pulmonology, 2006, 41(2): 105-121.
[40]	LIU D, DU J. A moving morphable components based shape reconstruction framework for electrical impedance tomography [J]. IEEE Transactions on Medical Imaging, 2019, 38(12): 2937-2948.
[5]	HUANG N N, MA Y X, ZHANG M Z, et al. Finite element modeling of human thorax based on MRI images for EIT image reconstructiong [J]. Journal of Shanghai Jiao Tong University (Science), 2021, 26(1): 33-39.
[41]	CHO K H, KIM S, LEE Y J. A fast EIT image reconstruction method for the two-phase flow visualization[J]. International Communications in Heat and Mass Transfer, 1999, 26(5): 637-646.
[6]	CAI Z X, LIAN B, WANG X D, et al.Dielectric characterization of normal thyroid lesions [J]. Biomedical Engineering and Clinical Medicine, 2017, 21(1): 17-21 (in Chinese).
[42]	RIBEIRO R R, FEITOSA A R S, DE SOUZA R E,et al. Reconstruction of electrical impedance tomography images using genetic algorithms and non-blind search [C]//2014 IEEE 11th International Symposium on Biomedical Imaging. Beijing, China: IEEE, 2014: 153-156.
[7]	WANG H, HE Y, YAN Q, et al. Correlation between the dielectric properties and biological activities of human ex vivo hepatic tissue [J]. Physics in Medicine and Biology, 2015, 60(6): 2603-2617.
[43]	RIBEIRO R R, FEITOSA A R S, DE SOUZA R E, et al. A modified differential evolution algorithm for the reconstruction of electrical impedance tomography images [C]//5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC). Salvador,Brazil: IEEE, 2014: 1-6.
[8]	YANG B, XU C H, DAI M, et al. An electrical impedance tomography imaging method incorporated with inhomogeneously-distributed skull resistivity [J].China Medical Devices, 2015, 30(8): 1-4 (in Chinese).
[44]	LIU S H, JIA J B, ZHANG Y D, et al. Image reconstruction in electrical impedance tomography based on structure-aware sparse Bayesian learning [J]. IEEE Transactions on Medical Imaging, 2018, 37(9): 2090-2102.
[9]	REN S J, TAN C, DONG F. Two phase flow visualization in an annular tube by an Electrical Resistance Tomography [C]//2012 IEEE International Conference on Imaging Systems and Techniques Proceedings.Manchester, UK: IEEE, 2012: 488-492.
[10]	REN S J, DONG F, TAN C. High-precision electrical resistance tomography with external and internal electrode arrays [C]//2011 IEEE International Instrumentation and Measurement Technology Conference.Hangzhou, China: IEEE, 2011: 1-6.
[11]	ZHAO Z, FRERICHS I, PULLETZ S, et al. The influence of image reconstruction algorithms on linear thorax EIT image analysis of ventilation [J]. Physiological Measurement, 2014, 35(6): 1083-1093.
[12]	OH T I, WI H, KIM D Y, et al. A fully parallel multifrequency EIT system with flexible electrode configuration:KHU Mark2 [J]. Physiological Measurement,2011, 32(7): 835-849.
[13]	SHI X T, YOU F S, HUO X Y, et al. High quality data acquisition system for electrical impedance tomography [J]. Journal of Data Acquisition and Processing,2010, 25(2): 259-263 (in Chinese).
[14]	WENG W L, DICKIN F J. Improved modified Newton-Raphson algorithm for electrical impedance tomography [J]. Electronics Letters, 1996, 32(3): 206-207.
[15]	MURAI T, KAGAWA Y. Electrical impedance computed tomography based on a finite element model [J].IEEE Transactions on Biomedical Engineering, 1985,BME-32(3): 177-184.
[16]	PIANA M, BERTERO M. Projected Landweber method and preconditioning [J]. Inverse Problems,1997, 13(2): 441-463.
[17]	M?LLER M F. A scaled conjugate gradient algorithm for fast supervised learning [J]. Neural Networks, 1993,6(4): 525-533.
[18]	VAUHKONEN M, VAD′ASZ D, KARJALAINEN P A,et al. Tikhonov regularization and prior information in electrical impedance tomography [J]. IEEE Transactions on Medical Imaging, 1998, 17(2): 285-293.
[19]	KIM H C, KIM K Y, PARK J W, et al. Electrical impedance tomography reconstruction algorithm using extended Kalman filter [C]//2001 IEEE International Symposium on Industrial Electronics Proceedings. Pusan,Korea: IEEE, 2001: 1677-1681.
[20]	MIAO L, MA Y, WANG J. ROI-based image reconstruction of electrical impedance tomography used to detect regional conductivity variation [J]. IEEE Transactions on Instrumentation and Measurement, 2014,63(12): 2903-2910.
[21]	CHEN X Y, ZHANG J. Using threshold correction method to improve the image quality of EIT [J]. Chinese Journal of Biomedical Engineering, 2011, 30(4):481-486 (in Chinese).
[22]	KNUDSEN K, LASSAS M, MUELLER J L, et al. Dbar method for electrical impedance tomography with discontinuous conductivities [J]. SIAM Journal on Applied Mathematics, 2007, 67(3): 893-913.
[23]	BERETTA E, MICHELETTI S, PEROTTO S, et al.Reconstruction of a piecewise constant conductivity on a polygonal partition via shape optimization in EIT[J]. Journal of Computational Physics, 2018, 353: 264-280.
[24]	CHEN X Y, SHI B, CHU M L, et al. Using neural network to improve the quality of EIT imaging [J].Journal of Tianjin University of Science & Technology,2016, 31(4): 74-78 (in Chinese).
[25]	ZHANG K, LI M K, YANG F, et al. Application of multiplicative regularization for electrical impedance tomography [C]//2017 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting. San Diego, CA, USA: IEEE, 2017: 27-28.
[26]	WANG H X, TANG L, CAO Z. An image reconstruction algorithm based on total variation with adaptive mesh refinement for ECT [J]. Flow Measurement and Instrumentation, 2007, 18(5/6): 262-267.
[27]	GONG B, SCHULLCKE B, KRUEGER-ZIOLEK S, et al. Higher order total variation regularization for EIT reconstruction [J]. Medical & Biological Engineering & Computing, 2018, 56(8): 1367-1378.
[28]	YANG Y, JIA J. An image reconstruction algorithm for electrical impedance tomography using adaptive group sparsity constraint [J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(9): 2295-2305.
[29]	YANG Y, WU H, JIA J. Image reconstruction for electrical impedance tomography using enhanced adaptive group sparsity with total variation [J]. IEEE Sensors Journal, 2017, 17(17): 5589-5598.
[30]	MARTINS T D C, TSUZUKI M D S G, CAMARGO E D L B D, et al. Interval Simulated Annealing applied to Electrical Impedance Tomography image reconstruction with fast objective function evaluation [J]. Computers & Mathematics with Applications, 2016, 72(5):1230-1243.
[31]	KNUDSEN K,MUELLER J, SILTANEN S. Numerical solution method for the dbar-equation in the plane [J].Journal of Computational Physics, 2004, 198(2): 500-517.
[32]	HAMILTON S J, HAUPTMANN A. Deep D-bar:Real-time electrical impedance tomography imaging with deep neural networks [J]. IEEE Transactions on Medical Imaging, 2018, 37(10): 2367-2377.
[33]	ALESSANDRINI G. Stable determination of conductivity by boundary measurements [J]. Applicable Analysis,1988, 27(1/2/3): 153-172.
[34]	HECK H. Stability estimates for the inverse conductivity problem for less regular conductivities [J]. Communications in Partial Differential Equations, 2009,34(2): 107-118.
[35]	HYV¨ONEN N, MUSTONEN L. Generalized linearization techniques in electrical impedance tomography [J].Numerische Mathematik, 2018, 140(1): 95-120.
[36]	CHUNG E T, CHAN T F, TAI X C. Electrical impedance tomography using level set representation and total variational regularization [J]. Journal of Computational Physics, 2005, 205(1): 357-372.
[37]	LIU D, SMYL D, DU J F. A parametric level set-based approach to difference imaging in electrical impedance tomography [J]. IEEE Transactions on Medical Imaging,2019, 38(1): 145-155.
[38]	LIU D, KHAMBAMPATI A K, DU J F. A parametric level set method for electrical impedance tomography [J]. IEEE Transactions on Medical Imaging, 2018,37(2): 451-460.
[39]	LIU D, GU D, SMYL D, et al. B-spline-based sharp feature preserving shape reconstruction approach for electrical impedance tomography [J]. IEEE Transactions on Medical Imaging, 2019, 38(11): 2533-2544.
[40]	LIU D, DU J. A moving morphable components based shape reconstruction framework for electrical impedance tomography [J]. IEEE Transactions on Medical Imaging, 2019, 38(12): 2937-2948.
[41]	CHO K H, KIM S, LEE Y J. A fast EIT image reconstruction method for the two-phase flow visualization[J]. International Communications in Heat and Mass Transfer, 1999, 26(5): 637-646.
[42]	RIBEIRO R R, FEITOSA A R S, DE SOUZA R E,et al. Reconstruction of electrical impedance tomography images using genetic algorithms and non-blind search [C]//2014 IEEE 11th International Symposium on Biomedical Imaging. Beijing, China: IEEE, 2014: 153-156.
[43]	RIBEIRO R R, FEITOSA A R S, DE SOUZA R E, et al. A modified differential evolution algorithm for the reconstruction of electrical impedance tomography images [C]//5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC). Salvador,Brazil: IEEE, 2014: 1-6.
[44]	LIU S H, JIA J B, ZHANG Y D, et al. Image reconstruction in electrical impedance tomography based on structure-aware sparse Bayesian learning [J]. IEEE Transactions on Medical Imaging, 2018, 37(9): 2090-2102.