Image reconstruction in electrical impedance tomography using neural network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86096789" target="_blank" >RIV/61989100:27240/15:86096789 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/CIBEC.2014.7020959" target="_blank" >http://dx.doi.org/10.1109/CIBEC.2014.7020959</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CIBEC.2014.7020959" target="_blank" >10.1109/CIBEC.2014.7020959</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Image reconstruction in electrical impedance tomography using neural network

Popis výsledku v původním jazyce

Electrical impedance tomography (EIT) imaging method is gaining its popularity due to ease of use and also non-invasiveness. The inner distribution of resistivity, which corresponds to different resistivity properties of different tissues, is estimated from voltage potentials measured on the boundary of inspected object. The major problem of EIT is how to reconstruct the image of inner resistivity. There are many approaches to solve this issue, which require more computational demands. The use of neuralnetwork to solve this non-linear problem addresses the demand to ease the implementation and lower the computational demands. In this article we adopted the use of Radial Basis Function (RBF) neural network for image reconstruction and compared it to reconstructed images obtained using EIDORS software. RBF network was created and trained using the Matlab and neural network toolbox. As training data the simulated measurement voltages and EIDORS difference reconstruction gained values of

Název v anglickém jazyce

Image reconstruction in electrical impedance tomography using neural network

Popis výsledku anglicky

Electrical impedance tomography (EIT) imaging method is gaining its popularity due to ease of use and also non-invasiveness. The inner distribution of resistivity, which corresponds to different resistivity properties of different tissues, is estimated from voltage potentials measured on the boundary of inspected object. The major problem of EIT is how to reconstruct the image of inner resistivity. There are many approaches to solve this issue, which require more computational demands. The use of neuralnetwork to solve this non-linear problem addresses the demand to ease the implementation and lower the computational demands. In this article we adopted the use of Radial Basis Function (RBF) neural network for image reconstruction and compared it to reconstructed images obtained using EIDORS software. RBF network was created and trained using the Matlab and neural network toolbox. As training data the simulated measurement voltages and EIDORS difference reconstruction gained values of

Druh

D - Stať ve sborníku

CEP obor

JB - Senzory, čidla, měření a regulace

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 7th Cairo International Biomedical Engineering Conference, CIBEC 2014

ISBN

978-1-4799-4412-5

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

39-42

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

New York

Místo konání akce

Giza

Datum konání akce

11. 12. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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