All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

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

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20201 - Electrical and electronic engineering

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2025

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Proceedings of the 15th International Conference on Measurement

  • ISBN

    978-80-69159-00-6

  • ISSN

  • e-ISSN

  • Number of pages

    4

  • Pages from-to

    2-5

  • Publisher name

  • Place of publication

    Smolenice

  • Event location

    Smolenice

  • Event date

    Jun 1, 2025

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article