Publication detail
Measurement Image Reconstruction in Electrical Impedance Tomography through 1D-UNet
KOUAKOUO NOMVUSSI, S. MIKULKA, J.
Original Title
Measurement Image Reconstruction in Electrical Impedance Tomography through 1D-UNet
Type
conference paper
Language
English
Original Abstract
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.
Keywords
Electrical Impedance Tomography, U-Net, Deep Learning, Image Reconstruction, Neural Networks
Authors
KOUAKOUO NOMVUSSI, S.; MIKULKA, J.
Released
1. 6. 2025
Location
Smolenice
ISBN
978-80-69159-00-6
Book
Proceedings of the 15th International Conference on Measurement
Pages from
2
Pages to
5
Pages count
4
BibTex
@inproceedings{BUT198064,
author="Serge Ayme {Kouakouo Nomvussi} and Jan {Mikulka}",
title="Measurement Image Reconstruction in Electrical Impedance Tomography through 1D-UNet",
booktitle="Proceedings of the 15th International Conference on Measurement",
year="2025",
pages="2--5",
address="Smolenice",
isbn="978-80-69159-00-6"
}