REVIEW PAPER
Use of electrical impedance tomography for lung volume reconstruction
 
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1
Netrix S.A.
 
2
WSEI University
 
3
Lublin University of Technology
 
 
Submission date: 2024-06-21
 
 
Acceptance date: 2024-07-17
 
 
Publication date: 2024-08-20
 
 
Corresponding author
Małgorzata Lalak - Dybała   

WSEI University
 
 
JoMS 2024;57(Numer specjalny 3):622-636
 
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ABSTRACT
The article presents a study of the application of electro-impedance tomography (EIT) in diagnosing lung capacity using the Tikhonov regularization method. The possibility of reconstructing the lungs to monitor the degree of air filling was investigated. The experiment included a series of tests using a torso phantom designed to simulate different states of the lungs - from fully inflated to fully deflated. Lung-filling states were manipulated in controlled scenarios to test nine main experimental conditions reflecting different lung-filling states. In addition, the quality of reconstruction was checked using various types of reference backgrounds. The results show significant differences in lung volume reconstructions depending on the lung filling state. The most successful reconstructions, which were obtained using the 'No phantom' background, provided the most explicit visualization of the lungs, reassuring the method's reliability. The experiments confirm the potential of EIT to distinguish between different lung states and reconstruct the degree of lung filling. The study also underscores the need to optimize the reference background to increase the precision of the images, especially for the left lung.
 
REFERENCES (30)
1.
Banasiak, R., Wajman, R., Jaworski, T., Fiderek, P., Fidos, H., Nowakowski, J., Sankowski, D. (2014). Study on two-phase flow regime visualization and identification using 3D electrical capacitance tomography and fuzzy-logic classification. Int. J. Multiph. Flow, 58, 1–14.
 
2.
Bangti, J., Maass, P. (2012). An Analysis of Electrical Impedance Tomography with Applications to Tikhonov Regularization. ESAIM COCV, 18, 1027–1048.
 
3.
Berowski, P., Filipowicz, S.F., Sikora, J., Wójtowicz, S. (2005). It is determining location of moisture area of the wall by 3D electrical impedance tomography. In Proceedings of the 4th World Congress in Industrial Process Tomography, Aizu, Japan, 5–8 September, pp. 214–219.
 
4.
Garbaa, H., Jackowska-Strumiłło, L., Grudzień, K., Romanowski, A. (2016). Application of Electrical Capacitance Tomography and Artificial Neural Networks to Rapid Estimation of Cylindrical Shape Parameters of Industrial Flow Structure. Arch. Electr. Eng. 65, 657–669.
 
5.
Kania, K., Mazurek, M., Rymarczyk, T. (2022). Application of finite difference method for measurement simulation in ultrasound transmission tomography. Applied Computer Science, 18(2), 101–109.
 
6.
Kak, A.C., Slaney, M. (1999). Principles of Computerized Tomographic Imaging, IEEE Press: New York, NY, USA.
 
7.
Kłosowski, G., Hoła, A., Rymarczyk, T., Skowron, Ł., Wołowiec, T., Kowalski, M. (2021). The Concept of Using LSTM to Detect Moisture in Brick Walls by Means of Electrical Impedance Tomography. Energies, 14, 7617.
 
8.
Kłosowski, G., Rymarczyk, T., Gola, A. (2018). Increasing the Reliability of Flood Embankments with Neural Imaging Method. Appl. Sci. 8, 1457.
 
9.
Kłosowski, G., Rymarczyk, T., Niderla, K., Rzemieniak, M., Dmowski, A., Maj, M. (2021). Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography. Energies, 14, 7269.
 
10.
Kryszyn, J., Smolik, W. (2017). Toolbox for 3D modelling and image reconstruction in electrical capacitance tomography. Inform. Control. Meas. Econ. Environ. Prot. 7, 137–145.
 
11.
Kryszyn, J., Wanta, D.M., Smolik, W.T. (2017) Gain Adjustment for Signal-to-Noise Ratio Improvement in Electrical Capacitance Tomography System EVT4. IEEE Sens. J, 17, 8107–8116.
 
12.
Majchrowicz, M., Kapusta, P., Jackowska-Strumiłło, L., Sankowski, D. (2017).Acceleration of image reconstruction process in the electrical capacitance tomography 3D in heterogeneous, multi-GPU system. Inform. Control. Meas. Econ. Environ. Prot., 7, 37–41.
 
13.
Mansouri S, Alharbi Y, Haddad F, Chabcoub S, Alshrouf A, Abd-Elghany AA. (2021). Electrical Impedance Tomography – Recent Applications and Developments. J Electr Bioimpedance. 20,12(1):50-62. doi: 10.2478/joeb-2021-0007.
 
14.
Mikulka, J. (2015). GPU-Accelerated Reconstruction of T2 Maps in Magnetic Resonance Imaging. Meas. Sci. Rev. 4, 210–218.
 
15.
Mojabi, P., Vetri, J.L. (2016). Development of an ultrasound tomography system: Preliminary results. J. Acoust. Soc. Am., 140, 3419.
 
16.
Morigi, M.P., Albertin, (2022). F. X-ray Digital Radiography and Computed Tomography. J. Imaging, 8, 119.
 
17.
Omeh DJ, Shlofmitz E. (2023) Angiography. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing, 2024 Jan–. PMID: 32491409.
 
18.
Przysucha, B., Rymarczyk, T., Wójcik, D., Woś, M., Vejar, A. (2020). Improving the Dependability of the ECG Signal for Classification of Heart Diseases. In Proceedings of the 50th Annual IEEE-IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S), Valencia, Spain, 2–29, 63–64.
 
19.
Rymarczyk, T., Kłosowski, G. (2019). Innovative methods of neural reconstruction for tomographic images in maintenance of tank industrial reactors. Eksploat. Niezawodn. Maint. Reliab., 21, 261–267.
 
20.
Rymarczyk, T., Kłosowski, G. (2019). The use of elastic net and neural networks in industrial process tomography. Przegląd Elektrotechniczny, 1, 61–64.
 
21.
Rymarczyk, T., Kłosowski, G., Hoła, A., Sikora, J., Wołowiec, T., Tchórzewski, P., Skowron, S. (2021). Comparison of Machine Learning Methods in Electrical Tomography for Detecting Moisture in Building Walls. Energies, 14, 2777.
 
22.
Rymarczyk, T., Kłosowski, G., Kozłowski, E. (2018) A Non-Destructive System Based on Electrical Tomography and Machine Learning to Analyze the Moisture of Buildings. Sensors, 18, 2285.
 
23.
Rymarczyk, T., Niderla, K., Kozłowski, E., Król, K., Wyrwisz, J.M., Skrzypek-Ahmed, S., Gołąbek, P. (2021). Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control. Energies, 14, 8116.
 
24.
Rymarczyk, T., Król, K., Kozłowski, E., Wołowiec, T. (2021) Cholewa-Wiktor, M., Bednarczuk, P. Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks. Energies, 14, 8081.
 
25.
Teh JL, Shabbir A, Yuen S, So JB. (2020). Recent advances in diagnostic upper endoscopy. World J Gastroenterol. 28,26(4):433-447. doi: 10.3748/wjg.v26.i4.433. PMID: 32063692, PMCID: PMC7002908.
 
26.
Wang, F., Marashdeh, Q., Fan, L.S., Warsito, W. (2010) Electrical Capacitance Volume Tomography: Design and Applications. Sensors, 10, 1890–1917.
 
27.
Wajman, R., Fiderek, P., Fidos, H., Jaworski, T., Nowakowski, J., Sankowski, D., Banasiak, R. (2013). Metrological evaluation of a 3D electrical capacitance tomography measurement system for two-phase flow fraction determination. Meas. Sci. Technol., 24, 065302.
 
28.
Wiskin, J., Malik, B., Borup, D., Pirshafiey, N., John Klock, J. (2020). Full wave 3D inverse scattering transmission ultrasound tomography in the presence of high contrast. Sci. Rep., 10, 20166.
 
29.
Zywica, A.R., Ziolkowski, M., Gratkowski, S. Detailed Analytical Approach to Solve the Magnetoacoustic Tomography with Magnetic Induction (MAT-MI) Problem for Three-Layer Objects. Energies 2020, 13, 6515.
 
30.
Xi, Y., Qiao, Z., Wang, W., Niu, L. (2020). Study of CT image reconstruction algorithm based on high order total variation. Optik, 204, 163814.
 
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ISSN:1734-2031
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