OOEIT: An open source object oriented electrical impedance tomography software package

Petri Kuusela; Marko Vauhkonen

doi:10.3934/ammc.2024010

Article Contents

2024, Volume 2, Issue 2: 243-261. Doi: 10.3934/ammc.2024010

This issue Previous Article A boundary integral equations approach to electrical impedance tomography: Experiments on the KTC2023 data Next Article Preface

OOEIT: An open source object oriented electrical impedance tomography software package

Petri Kuusela^1, , and
Marko Vauhkonen^1,

1.
University of Eastern Finland, Department of Technical Physics, Finland

^*Corresponding author: Petri Kuusela
^*Corresponding author: Petri Kuusela

Received: April 2024

Revised: May 2024

Early access: June 14, 2024

Published online: June 14, 2024

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Abstract

Electrical impedance tomography (EIT) is an imaging modality based on electrical currents injected through the imaging domain and voltages measured on the boundary. It has a wide variety of applications ranging e.g. from medical imaging to industrial process monitoring. Computing the EIT reconstruction from the measurements is a severely ill-posed inverse problem, and hence it is not a straightforward task. Therefore, the EIT reconstruction algorithms remain an active area of research. To facilitate this research, we present in this article object-oriented EIT (OOEIT), an open source software package for MATLAB. It is designed for ease of use for those with no deep understanding of EIT, while also featuring a modular structure that allows for straightforward customization for those interested in developing their own algorithms. OOEIT includes implementations for both 2D and 3D computations, for both static and dynamic reconstructions, and supports user defined meshes. As examples, we present reconstructions computed from the Kuopio Tomography Challenge (KTC2023) data set.

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Mathematics Subject Classification: Primary: 78-04, 78A46; Secondary: 90-04.

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Figure 1. A high-level diagram of the basic OOEIT framework structure for computing EIT reconstructions. An object inside another object means the inner object is a property of the outer object

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Figure 2. Example reconstructions and their segmentations computed from KTC2023 data. The reconstructions are two-dimensional conductivities in units of S. The segmentations and ground truths are color coded such that the background is blue, conductive inclusions are yellow, and resistive inclusions are cyan

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Figure 3. Uncertainty quantification (UQ) plots for the reconstruction of Target 1 (shown in (a)) computed using the total variation prior. (b) shows the spatial variation of the width of the 95% credible interval, and (c) shows the estimated conductivities and the 95% credible interval along the black line shown in (a)

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Figure 4. UQ plots for the reconstruction of Target 1 (shown in (a)) computed using the smoothness prior. (b) shows the spatial variation of the width of the 95% credible interval, and (c) shows the estimated conductivities and the 95% credible interval along the black line shown in (a)

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References

[1]	A. Adler, et al., GREIT: A unified approach to 2D linear EIT reconstruction of lung images, Physiological Measurement, 30 (2009), S35.
[2]	A. Adler and D. Holder, Electrical Impedance Tomography: Methods, History and Applications, 2$^{nd}$ edition, Taylor & Francis Group LLC, Florida, 2022.
[3]	A. Adler and W. Lionheart, Uses and abuses of EIDORS: An extensible software base for EIT, Physiological Measurement, 27 (2006), S25.
[4]	J. Bardsley, A. Seppänen, A. Solonen, H. Haario and J. Kaipio, Randomize-then-optimize for sampling and uncertainty quantification in electrical impedance tomography, SIAM/ASA Journal on Uncertainty Quantification, 3 (2015), 1136-1158. doi: 10.1137/140978272.
[5]	M. Bodenstein, M. David and K. Markstaller, Principles of electrical impedance tomography and its clinical application, Critical Care Medicine, 37 (2009), 713-724.
[6]	A. Chambolle and T. Pock, A first-order primal-dual algorithm for convex problems with applications to imaging, Journal of Mathematical Imaging and Vision, 40 (2011), 120-145. doi: 10.1007/s10851-010-0251-1.
[7]	K.-S. Cheng, D. Isaacson, J. Newell and D. Gisser, Electrode models for electric current computed tomography, IEEE Transactions on Biomedical Engineering, 36 (1989), 918-924.
[8]	G. González, J. Huttunen, V. Kolehmainen, A. Seppänen and M. Vauhkonen, Experimental evaluation of 3D electrical impedance tomography with total variation prior, Inverse Problems in Science and Engineering, 24 (2016), 1411-1431.
[9]	M. Hallaji, A. Seppänen and M. Pour-Ghaz, Electrical impedance tomography-based sensing skin for quantitative imaging of damage in concrete, Smart Materials and Structures, 23 (2014), 085001.
[10]	L. Heikkinen, J. Kourunen, T. Savolainen, P. Vauhkonen, J. Kaipio and M. Vauhkonen, Real time three-dimensional electrical impedance tomography applied in multiphase flow imaging, Measurement Science and Technology, 17 (2006), 2083-2087.
[11]	L. Heikkinen, T. Vilhunen, R. West and M. Vauhkonen, Simultaneous reconstruction of electrode contact impedances and internal electrical properties: Ⅱ. Laboratory experiments, Measurement Science and Technology, 13 (2002), 1855.
[12]	J. Jauhiainen, P. Kuusela, A. Seppänen and T. Valkonen, Relaxed Gauss-Newton methods with applications to electrical impedance tomography, SIAM Journal on Imaging Sciences, 13 (2020), 1415-1445. doi: 10.1137/20M1321711.
[13]	J. Jauhiainen, M. Pour-Ghaz, T. Valkonen and A. Seppänen, Nonplanar sensing skins for structural health monitoring based on electrical resistance tomography, Computer-Aided Civil and Infrastructure Engineering, 36 (2021), 1488-1507.
[14]	J. Kaipio and E. Somersalo, Statistical and Computational Inverse Problems, Springer-Verlag, New York, 2005.
[15]	W. Lionheart, S. Arridge, M. Schweiger, M. Vauhkonen and J. Kaipio, Electrical impedance and diffuse optical tomography reconstruction software, Proceedings of the 1st World Congress on Industrial Process Tomography, Derbyshire, UK, (1999), 474-477.
[16]	A. Lipponen, A. Seppänen and J. Kaipio, Electrical impedance tomography imaging with reduced-order model based on proper orthogonal decomposition, Journal of Electronic Imaging, 22 (2013), 023008.
[17]	B. Liu, et al., pyEIT: A python based framework for Electrical Impedance Tomography, SoftwareX, 7 (2018), 304-308.
[18]	D. Liu, V. Kolehmainen, S. Siltanen and A. Seppänen, A nonlinear approach to difference imaging in EIT; assessment of the robustness in the presence of modelling errors, Inverse Problems, 31 (2015), 035012. doi: 10.1088/0266-5611/31/3/035012.
[19]	E. Malone, M. Jehl, S. Arridge, T. Betcke and D. Holder, Stroke type differentiation using spectrally constrained multifrequency EIT: Evaluation of feasibility in a realistic head model, Physiological Measurement, 35 (2014), 1051-1066.
[20]	T. C. Matrins, et al., A review of electrical impedance tomography in lung applications: Theory and algorithms for absolute images, Annual Reviews in Control, 48 (2019), 442-471. doi: 10.1016/j.arcontrol.2019.05.002.
[21]	B. McDermott, M. O'Halloran, J. Avery and E. Porter, Bi-frequency symmetry difference EIT – feasibility and limitations of application to stroke diagnosis, IEEE Journal of Biomedical and Health Informatics, 24 (2020), 2409-2419.
[22]	N. Otsu, A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics, 9 (1979), 62-66.
[23]	T. Ouypornkochagorn, Constrained modeling for image reconstruction in the application of Electrical Impedance Tomography to the head, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia, (2017), 548-551.
[24]	N. Polydorides and W. Lionheart, A Matlab toolkit for three-dimensional electrical impedance tomography: A contribution to the electrical impedance and diffuse optical reconstruction software project, Measurement Science and Technology, 13 (2002), 1871.
[25]	C. Putensen, B. Hentze, S. Muenster and T. Muders, Electrical impedance tomography for cardio-pulmonary monitoring, Journal of Clinical Medicine, 8 (2019), 1176.
[26]	A. Seppänen, M. Hallajia and M. Pour-Ghaz, A functionally layered sensing skin for the detection of corrosive elements and cracking, Structural Health Monitoring, 16 (2017), 215-224.
[27]	E. Somersalo, M. Cheney and D. Isaacson, Existence and uniqueness for electrode models for electric current computed tomography, SIAM Journal on Applied Mathematics, 52 (1992), 1023-1040. doi: 10.1137/0152060.
[28]	J. Toivanen, A. Hänninen, T. Savolainen, N. Forss and V. Kolehmainen, 8 - monitoring hemorrhagic strokes using EIT, in Bioimpedance and Spectroscopy, Academic Press, (2021), 271-298.
[29]	T. Valkonen, A primal-dual hybrid gradient method for nonlinear operators with applications to MRI, Inverse Problems, 30 (2014), 055012. doi: 10.1088/0266-5611/30/5/055012.
[30]	M. Vauhkonen, W. Lionheart, L. Heikkinen, P. Vauhkonen and J. Kaipio, A MATLAB package for the EIDORS project to reconstruct two-dimensional EIT images, Physiological Measurement, 22 (2001), 107-111.
[31]	P. Vauhkonen, M. Vauhkonen, T. Savolainen and J. Kaipio, Three-dimensional electrical impedance tomography based on the complete electrode model, IEEE Transactions on Biomedical Imaging, 46 (1999), 1150-1160.
[32]	T. Vilhunen, J. Kaipio, P. Vauhkonen, T. Savolainen and M. Vauhkonen, Simultaneous reconstruction of electrode contact impedances and internal electrical properties: I. theory, Measurement Science and Technology, 13 (2002), 1848.
[33]	A. Voss, Imaging Moisture Flows in Cement-Based Materials Using Electrical Capacitance Tomography, Ph.D thesis, University of Eastern Finland in Kuopio, 2020.
[34]	A. Voss, P. Hosseini, M. Pour-Ghaz, M. Vauhkonen and A. Seppänen, Three-dimensional electrical capacitance tomography-A tool for characterizing moisture transport properties of cement-based materials, Materials & Design, 181 (2019), 107967.
[35]	Kuopio tomography challenge home page, 2024, Available from: https://www.fips.fi/KTC2023.php.