Measurement Image Reconstruction in Electrical Impedance Tomography through 1D-UNet

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0198064" target="_blank" >RIV/00216305:26220/26:0198064 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.23919/MEASUREMENT66999.2025.11078744" target="_blank" >http://dx.doi.org/10.23919/MEASUREMENT66999.2025.11078744</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/MEASUREMENT66999.2025.11078744" target="_blank" >10.23919/MEASUREMENT66999.2025.11078744</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Measurement Image Reconstruction in Electrical Impedance Tomography through 1D-UNet

Popis výsledku v původním jazyce

This paper presents a deep learning approach for image reconstruction in Electrical Impedance Tomography using a one-dimensional U-Net model. The model’s performance is evaluated against traditional methods such as the Total Variation and Gauss-Newton algorithms. Experimental results demonstrate that 1D-UNet consistently achieves superior reconstruction accuracy, particularly in noisy environments. In noise-free conditions, the model attains higher correlation coefficients and structural similarity values than conventional approaches, preserving fine details effectively. Under noisy conditions (30 dB and 60 dB), 1D-UNet maintains a significantly higher correlation and structural similarity, demonstrating its robustness. The strong generalization and adaptability of the proposed method underscore its potential for enhancing tomographic imaging applications in biomedical diagnostics, industrial process monitoring.

Název v anglickém jazyce

Measurement Image Reconstruction in Electrical Impedance Tomography through 1D-UNet

Popis výsledku anglicky

This paper presents a deep learning approach for image reconstruction in Electrical Impedance Tomography using a one-dimensional U-Net model. The model’s performance is evaluated against traditional methods such as the Total Variation and Gauss-Newton algorithms. Experimental results demonstrate that 1D-UNet consistently achieves superior reconstruction accuracy, particularly in noisy environments. In noise-free conditions, the model attains higher correlation coefficients and structural similarity values than conventional approaches, preserving fine details effectively. Under noisy conditions (30 dB and 60 dB), 1D-UNet maintains a significantly higher correlation and structural similarity, demonstrating its robustness. The strong generalization and adaptability of the proposed method underscore its potential for enhancing tomographic imaging applications in biomedical diagnostics, industrial process monitoring.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 15th International Conference on Measurement

ISBN

978-80-69159-00-6

ISSN

—

e-ISSN

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Počet stran výsledku

4

Strana od-do

2-5

Název nakladatele

—

Místo vydání

Smolenice

Místo konání akce

Smolenice

Datum konání akce

1. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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