References
- Amini M, Hisdal J, and Kalvøy H. Applications of bioimpedance measurement techniques in tissue engineering. Journal of Electrical Bioimpedance 2018; 9:142–58. DOI:
https://doi.org/10.2478/joeb-2018-0019 - Adler A, Amato MB, Arnold JH, Bayford R, Bodenstein M, Böhm SH, Brown BH, Frerichs I, Stenqvist O, Weiler N, et al. Whither lung EIT: where are we, where do we want to go and what do we need to get there? Physiological measurement 2012; 33:679. DOI:
https://doi.org/10.1088/0967-3334/33/5/679 - Harikumar R, Prabu R, and Raghavan S. Electrical impedance tomography (EIT) and its medical applications: A review. Int. J. Soft Comput. Eng 2013; 3:193–8. Available from:
https://www.ijsce.org/portfolio-item/d1821093413/ - Bera TK. Applications of electrical impedance tomography (EIT): a short review. IOP Conference Series: Materials Science and Engineering. Vol. 331. 1. IOP Publishing. 2018 :012004. DOI:
https://doi.org/10.1088/1757-899X/331/1/012004 - Ching CTS and Chen JH. A non-invasive, bioimpedance-based 2-dimensional imaging system for detection and localization of pathological epithelial tissues. Sensors and Actuators B: Chemical 2015; 206:319–26. DOI:
https://doi.org/10.1016/j.snb.2014.09.072 - Chitturi V and Farrukh N. Spatial resolution in electrical impedance tomography: A topical review. Journal of Electrical Bioimpedance 2017; 8:66–78. DOI:
https://doi.org/10.5617/jeb.3350 - Aristovich K. Opinion: The future of electrical impedance tomography. Journal of Electrical Bioimpedance 2022; 13:1–3. DOI:
https://doi.org/10.2478/joeb-2022-0001 - Soleimani M, Gómez-Laberge C, and Adler A. Imaging of conductivity changes and electrode movement in EIT. Physiological measurement 2006; 27:S103. DOI:
https://doi.org/10.1088/0967-3334/27/5/S09 - Mueller JL, Isaacson D, and Newell JC. Reconstruction of conductivity changes due to ventilation and perfusion from EIT data collected on a rectangular electrode array. Physiological measurement 2001; 22:97. DOI:
https://doi.org/10.1088/0967-3334/22/1/313 - Grewal PK, Shokoufi M, Liu J, Kalpagam K, and Kohli KS. Electrical characterization of bolus material as phantom for use in electrical impedance and computed tomography fusion imaging. Journal of Electrical Bioimpedance 2014; 5:34–9. DOI:
https://doi.org/10.5617/jeb.781 - Buendia R, Gil-Pita R, and Seoane F. Cole parameter estimation from the modulus of the electrical bioimpeadance for assessment of body composition. A full spectroscopy approach. Journal of Electrical Bioimpedance 2011; 2:72–8. DOI:
https://doi.org/10.5617/jeb.197 - Setyawan G, Sejati PA, Ibrahim KA, and Takei M. Breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT–GRTD). Journal of Electrical Bioimpedance 2024; 15:99–106. DOI:
https://doi.org/10.2478/joeb-2024-0011 - Lionheart WR. EIT reconstruction algorithms: pitfalls, challenges and recent developments. Physiological measurement 2004; 25:125. DOI:
https://doi.org/10.1088/0967-3334/25/1/021 - Adler A, Grychtol B, and Bayford R. Why is EIT so hard, and what are we doing about it. Physiological measurement 2015; 36:1067–73. DOI:
https://doi.org/10.1088/0967-3334/36/6/1067 - Vallecillo-Bustos A, Compton AT, Swafford SH, Renna ME, Thorsen T, Stavres J, and Graybeal AJ. The effect of postural orientation on body composition and total body water estimates produced by smartwatch bioelectrical impedance analysis: an intra- and inter-device evaluation. Journal of Electrical Bioimpedance 2024; 15:89–98. DOI:
https://doi.org/10.2478/joeb-2024-0010 - Dowrick T, Dos Santos GS, Vongerichten A, and Holder D. Parallel, multi frequency EIT measurement, suitable for recording impedance changes during epilepsy. Journal of Electrical Bioimpedance 2015; 6:37–43. DOI:
https://doi.org/10.5617/jeb.2573 - Brazey B, Haddab Y, and Zemiti N. Robust imaging using electrical impedance tomography: review of current tools. Proceedings of the Royal Society A 2022; 478:20210713. DOI:
https://doi.org/10.1098/rspa.2021.0713 - Bera TK, Biswas SK, Rajan K, and Nagaraju J. Improving Image Quality in Electrical Impedance Tomography (EIT) Using Projection Error Propagation-Based Regularization (PEPR) Technique: A Simulation Study. Journal of Electrical Bioimpedance 2011; 2:2–12. DOI:
https://doi.org/10.5617/jeb.158 - Bera TK, Biswas SK, Rajan K, and Nagaraju J. Improving Conductivity Image Quality Using Block Matrix-based Multiple Regularization (BMMR) Technique in EIT: A Simulation Study. Journal of Electrical Bioimpedance 2011; 2:33–47. DOI:
https://doi.org/10.5617/jeb.170 - Zhao Z, Frerichs I, Pulletz S, Müller-Lisse U, and Möller K. The influence of image reconstruction algorithms on linear thorax EIT image analysis of ventilation. Physiological measurement 2014; 35:1083. DOI:
https://doi.org/10.1088/0967-3334/35/6/1083 - Goharian M, Bruwer MJ, Jegatheesan A, Moran GR, and MacGregor JF. A novel approach for EIT regularization via spatial and spectral principal component analysis. Physiological Measurement 2007; 28:1001. DOI:
https://doi.org/10.1088/0967-3334/28/9/003 - Dai T, Soleimani M, and Adler A. EIT image reconstruction with four dimensional regularization. Medical & Biological Engineering & Computing 2008; 46:889–99. DOI:
https://doi.org/10.1007/s11517-008-0371-6 - Martins TC and Tsuzuki MSG. EIT image regularization by a new multi-objective simulated annealing algorithm. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE. 2015 :4069–72. DOI:
https://doi.org/10.1109/EMBC.2015.7319288 - Braun F, Proença M, Lemay M, Bertschi M, Adler A, Thiran JP, and Solà J. Limitations and challenges of EIT-based monitoring of stroke volume and pulmonary artery pressure. Physiological measurement 2018; 39:014003. DOI:
https://doi.org/10.1088/1361-6579/aa9828 - Zong Z, Wang Y, and Wei Z. A review of algorithms and hardware implementations in electrical impedance tomography. Progress In Electromagnetics Research 2020; 169:59–71. DOI:
https://doi.org/10.2528/PIER20120401 - Liu J, Lin L, Zhang W, and Li G. A novel combined regularization algorithm of total variation and Tikhonov regularization for open electrical impedance tomography. Physiological measurement 2013; 34:823. DOI:
https://doi.org/10.1088/0967-3334/34/7/823 - Jin B and Maass P. An analysis of electrical impedance tomography with applications to Tikhonov regularization. ESAIM: Control, Optimisation and Calculus of Variations 2012; 18:1027–48. DOI:
https://doi.org/10.1051/cocv/2011193 - Augustin X, Kircher M, Dössel O, Stender B, Bluth T, and Gama de Abreu M. Estimating regional pulmonary blood flow in EIT with regularized deconvolution with a Tikhonov regularization. Current Directions in Biomedical Engineering 2020; 6:60–3. DOI:
https://doi.org/10.1515/cdbme-2020-3016 - Widodo A et al. Experimental study of one step linear Gauss-Newton algorithm for improving the quality of image reconstruction in high-speed Electrical Impedance Tomography (EIT). Journal of Physics: Conference Series. Vol. 1120. 1. IOP Publishing. 2018 :012067. DOI:
https://doi.org/10.1088/1742-6596/1120/1/012067 - Jauhiainen J, Kuusela P, Seppanen A, and Valkonen T. Relaxed Gauss–Newton methods with applications to electrical impedance tomography. SIAM Journal on Imaging Sciences 2020; 13:1415–45. DOI:
https://doi.org/10.1137/20M1321711 - Gong B, Schullcke B, Krueger-Ziolek S, Vauhkonen M, Wolf G, Mueller-Lisse U, and Moeller K. EIT imaging regularization based on spectral graph wavelets. IEEE transactions on medical imaging 2017; 36:1832–44. DOI:
https://doi.org/10.1109/TMI.2017.2716825 - Martin S and Choi CT. Nonlinear electrical impedance tomography reconstruction using artificial neural networks and particle swarm optimization. IEEE transactions on magnetics 2015; 52:1–4. DOI:
https://doi.org/10.1109/TMAG.2015.2488901 - Dumdum CRM, Aleluya ERM, Galangque CMJ, Haim SH, and Salaan CJ. A hybrid reconstruction algorithm for Web. EIT: A difference electrical impedance tomography simulation system. 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM). IEEE. 2019 :1–6. DOI:
https://doi.org/10.1109/HNICEM48295.2019.9073594 - Cheney M, Isaacson D, Newell JC, Simske S, and Goble J. NOSER: An algorithm for solving the inverse conductivity problem. International Journal of Imaging systems and technology 1990; 2:66–75. DOI:
https://doi.org/10.1002/ima.1850020203 - Wang Q, Chen X, Wang D, Wang Z, Zhang X, Xie N, and Liu L. Regularization solver guided FISTA for electrical impedance tomography. Sensors 2023; 23:2233. DOI:
https://doi.org/10.3390/s23042233 - Rao L, He R, Wang Y, Yan W, Bai J, and Ye D. An efficient improvement of modified Newton-Raphson algorithm for electrical impedance tomography. IEEE transactions on magnetics 1999; 35:1562–5. DOI:
https://doi.org/10.1109/20.767269 - Zibetti MV, Helou ES, and Pipa DR. Accelerating overrelaxed and monotone fast iterative shrinkage-thresholding algorithms with line search for sparse reconstructions. IEEE Transactions on Image Processing 2017; 26:3569–78. DOI:
https://doi.org/10.1109/TIP.2017.2699483 - Zhang M, Wu Q, Liu Y, and Zheng J. Optimization of depth from defocus based on iterative shrinkage thresholding algorithm. Web Information Systems and Applications: 15th International Conference, WISA 2018, Taiyuan, China, September 14–15, 2018, Proceedings 15. Springer. 2018 :131–44. DOI:
https://doi.org/10.1007/978-3-030-02934-0_13 - Bera TK and Nagaraju J. A FEM-based forward solver for studying the forward problem of electrical impedance tomography (EIT) with a practical biological phantom. 2009 IEEE International Advance Computing Conference. IEEE. 2009 :1375–81. DOI:
https://doi.org/10.1109/IADCC.2009.4809217 - Johnstone PR and Moulin P. Convergence of an inertial proximal method for l 1-regularized least-squares. 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE. 2015 :3566–70. DOI:
https://doi.org/10.1109/ICASSP.2015.7178635 - Chen Z, Nagy JG, Xi Y, and Yu B. Structured FISTA for image restoration. Numerical Linear Algebra with Applications 2020; 27:e2278. DOI:
https://doi.org/10.1002/nla.2278 - Parikh N, Boyd S, et al. Proximal algorithms. Foundations and trends® in Optimization 2014; 1:127–239. DOI:
https://doi.org/10.1561/2400000003 - Ryu EK and Yin W. Proximal-proximal-gradient method. arXiv preprint arXiv:1708.06908 2017. DOI:
https://doi.org/10.48550/arXiv.1708.06908 - Wadayama T and Takabe S. Chebyshev periodical successive over-relaxation for accelerating fixed-point iterations. IEEE Signal Processing Letters 2021; 28:907–11. DOI:
https://doi.org/10.1109/LSP.2021.3073620 - Guo K, Yuan X, and Zeng S. Convergence analysis of ISTA and FISTA for “strongly+ semi” convex programming. Optimization Online 2016. Available from:
https://optimization-online.org/?p=14051 - Mustafa WA, Yazid H, Jaafar M, Zainal M, Abdul-Nasir AS, and Mazlan N. A review of image quality assessment (iqa): Snr, gcf, ad, nae, psnr, me. Journal of advanced research in computing and applications 2017; 7:1–7. Available from:
https://api.semanticscholar.org/CorpusID:251832016 - Adler A, Arnold JH, Bayford R, Borsic A, Brown B, Dixon P, Faes TJ, Frerichs I, Gagnon H, Gärber Y, et al. GREIT: a unified approach to 2D linear EIT reconstruction of lung images. Physiological measurement 2009; 30:S35. DOI:
https://doi.org/10.1088/0967-3334/30/6/S03 - Adler A and Lionheart WR. Uses and abuses of EIDORS: an extensible software base for EIT. Physiological measurement 2006; 27:S25. DOI:
https://doi.org/10.1088/0967-3334/27/5/s03 - Adler A, Boyle A, Crabb MG, Grychtol B, Lionheart W, Tregidgo H, and Yerworth R. EIDORS Version 3.9. Proc. 18th Int. Conf. on Biomed. Applications of EIT. Thayer School of Engineering at Dartmouth. 2017. Available from:
https://doi.org/10.5281/zenodo.583266