REVIEW PAPER
3D modeling of the urinary bladder using electrical impedance tomography: advanced reconstruction algorithms and medical applications
 
More details
Hide details
1
WSEI University, Lublin, Poland
 
2
Lublin University of Technology, Lublin, Poland
 
 
Submission date: 2024-06-28
 
 
Acceptance date: 2024-07-19
 
 
Publication date: 2024-08-20
 
 
Corresponding author
Katarzyna Iskra   

WSEI University, Lublin, Poland
 
 
JoMS 2024;57(Numer specjalny 3):713-722
 
KEYWORDS
TOPICS
_Other
 
ABSTRACT
Purpose: The research presented in this paper was conducted to obtain a detailed 3D model of the urinary bladder using electrical impedance tomography, a noninvasive tomographic technique. Methods: Electrical impedance tomography (EIT) is an imaging technique that measures electrical impedance inside the human body. Many methods, including those based on physical models and machine learning, are used to reconstruct the considered 3D object using EIT. The work focuses on the Gauss-Newton algorithm in its generalized form. Results: Three-dimensional reconstructions of the urinary bladder were obtained. The models are built with high accuracy and can be processed by subsequent algorithms. Discussion: The constructed models can serve as the basis for correct diagnosis in medicine and as research material for subsequent work, for example, on the possibilities of 3D printing. Possible methods of obtaining even higher-quality reconstruction also remain to be considered.
 
REFERENCES (22)
1.
Anwar, S., Bunker, M., Henry, T., Kouretas, P., Harris, I., Agarwal, A. (2023). 3D Modeling in Congenital Cardiac Interventions. 367–375. https://doi.org/10.1007/978-3-....
 
2.
Beetz, M., Banerjee, A., Grau, V. (2024). Modeling 3D Cardiac Contraction and Relaxation With Point Cloud Deformation Networks. IEEE Journal of Biomedical and Health Informatics PP, 1–10. https://doi.org/10.1109/JBHI.2....
 
3.
Chen, Y. (2024). Research on prosthesis customization with 3D modeling and printing technology. Theoretical and Natural Science 29, 158–163. https://doi.org/10.54254/2753-....
 
4.
Dziadosz, M., Mazurek, M., Stefaniak, B., Wójcik, D., Gauda, K. (2024). A comparative study of selected machine learning algorithms for electrical impedance tomography. ELECTROTECHNICAL REVIEW 1(4), 239–242. https://doi.org/10.15199/48.20....
 
5.
Gholami, A. (2024). An extended Gauss-Newton method for full waveform inversion. GEOPHYSICS 89, 1–55. https://doi.org/10.1190/geo202....
 
6.
Hanif, D. (2024). Gauss-Newton Method for Feedforward Artificial Neural Networks.
 
7.
Hegazy, M., Cho, Myung, Cho, Min, Lee, S. (2024). 3D Digital Modeling of Dental Casts from Their 3D CT Images with Scatter and Beam-Hardening Correction. Sensors 24. https://doi.org/10.3390/s24061....
 
8.
Kozłowski, E., Rymarczyk, T., Kłosowski, G., Cieplak, T. (2020). Logistic regression in image reconstruction in electrical impedance tomography. Przeglad Elektrotechniczny 5, 95–98.
 
9.
Kudashkina, A., Kamyshanskaya, I., Pavelets, K., Rusanov, D., Kalyuzhnyy, S. (2024). 3D-modeling Capabilities in Assessing Resectability of Pancreatic Head Tumors. Journal of radiology and nuclear medicine 104, 244–254. https://doi.org/10.20862/0042-....
 
10.
Lebedeva, A., Ryabov, V. (2022). Method of moments in the problem of inversion of the Laplace transform and its regularization. Vestnik of Saint Petersburg University. Mathematics. Mechanics. Astronomy 9, 46–52. https://doi.org/10.21638/spbu0....
 
11.
Liu, Q., Wang, S., Wei, Y. (2024). A Gauss–Newton method for mixed least squares-total least squares problems. Calcolo 61, 1–27. https://doi.org/10.1007/s10092....
 
12.
Loke, M., Dahlin, T. (2002). A comparison of the Gauss-Newton and quasi-Newton methods in resistivity imaging inversion. Journal of Applied Geophysics 49, 149–162. https://doi.org/10.1016/S0926-....
 
13.
Mahale, P., Singh, A. (2024). Convergence analysis of simplified Gauss–Newton iterative method under a heuristic rule. Proceedings – Mathematical Sciences 134. https://doi.org/10.1007/s12044....
 
14.
Maréchal, P., Triki, F., Simo, W. (2023). Regularization of the inverse Laplace transform by mollification. Inverse Problems 40. https://doi.org/10.1088/1361-6....
 
15.
Morrison, N. (2013). Tracking Filter Engineering: The Gauss-Newton and polynomial filters. https://doi.org/10.1049/PBRA02....
 
16.
Pillutla, K., Roulet, V., Kakade, S., Harchaoui, Z. (2023). Modified Gauss-Newton Algorithms under Noise.
 
17.
Przysucha, B., Wójcik, D., Rymarczyk, T., Król, K., Kozłowski, E., Gąsior, M. (2023). Analysis of Reconstruction Energy Efficiency in EIT and ECT 3D Tomography Based on Elastic Net. Energies 16(3), 1490. https://doi.org/10.3390/en1603....
 
18.
Rymarczyk, T., Mazurek, M., Hyka, O., Wójcik, D., Dziadosz, M., Kowalski, M. (2024). Poster Abstract: A Wearable for Non-Invasive Monitoring and Diagnosing Functional Disorders of the Lower Urinary Tract, in: Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems, SenSys ’23. Association for Computing Machinery, New York, NY, USA, 510–511. https://doi.org/10.1145/362568....
 
19.
Rymarczyk, T., Tchorzewski, P. (2018). Implementation 3D level set method to solve inverse problem in EIT. 159–161. https://doi.org/10.1109/IIPHDW....
 
20.
Siregar, R., Tulus, T., Ramli, M. (2018). Analysis Local Convergence of Gauss-Newton Method. IOP Conference Series: Materials Science and Engineering 300, 012044. https://doi.org/10.1088/1757-8....
 
21.
Sokolov, Y., Topilin, O., Airapetyan, M., Sukhodolskaya, O., Vydysh, S. (2024). The clinical use of 3D-modeling in pediatric surgery. Archives of Pediatrics and Pediatric Surgery 1, 24–30. https://doi.org/10.31146/2949-....
 
22.
Yudin, N. (2021). Modified Gauss-Newton method for solving a smooth system of nonlinear equations. Computer Research and Modeling 13, 697–723. https://doi.org/10.20537/2076-....
 
eISSN:2391-789X
ISSN:1734-2031
Journals System - logo
Scroll to top