Image Reconstruction in Electrical Impedance Tomography through 1D-Convolutional Neural Network

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0198079" target="_blank" >RIV/00216305:26220/26:0198079 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Image Reconstruction in Electrical Impedance Tomography through 1D-Convolutional Neural Network
Original language description
This paper presents a comparative analysis of image reconstruction performance using a 1D-Convolutional Neural Network (1D-CNN) against the Total Variation algorithm and the Gauss-Newton algorithm. The evaluation, conducted across multiple tests conditions, demonstrates that the 1D-CNN consistently outperforms both conventional methods in terms of correlation coefficient and structural similarity index (SSIM). In noise-free scenarios, the 1D-CNN achieves significantly higher correlation and SSIM values, indicating superior reconstruction accuracy. Furthermore, in the presence of noise (30 dB and 60 dB), the performance of the Total Variation and Gauss-Newton algorithms deteriorates considerably, whereas the 1D-CNN maintains high correlation and SSIM values, demonstrating strong robustness to noise. These findings highlight the effectiveness of deep learning-based approaches for image reconstruction, making the 1D-CNN a promising alternative to traditional reconstruction techniques.
Czech name
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Czech description
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Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings I of the 31st Conference STUDENT EEICT 2025
ISBN
978-80-214-6321-9
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
316-320
Publisher name
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Place of publication
Brno
Event location
Brno
Event date
Apr 29, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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