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UNet model in image reconstruction for electrical impedance tomography

cris.lastimport.scopus2024-09-17T01:31:25Z
dc.abstract.enThis paper presents a new algorithm where the UNet convolutional neural network was used to correct deterministic algorithm results, as was is another similar solution using the DBar deterministic algorithm. Instead of the DBar algorithm, another EIT reconstruction algorithm was used in the context cooperation with impedance tomography to extract details in EIT reconstruction. The algorithm uses machine learning to improve the tomographic images obtained with the deterministic algorithm. The final result contains much less noise, and the position of the objects is much better defined, unlike in the deterministic approach. Furthermore, the paper shows how the reconstruction obtained with the hybrid tomograph can be improved to show more details. This paper aims to present a solution that will be used in the context of medical tomography, where the EIT system and the developed algorithm will be used to obtain high-resolution tomography images of the bladder.
dc.affiliationTransportu i Informatyki
dc.contributor.authorŁukasz Maciura
dc.contributor.authorTomasz Rymarczyk
dc.contributor.authorMichał Maj
dc.contributor.authorDariusz Wójcik
dc.date.accessioned2024-08-05T07:08:59Z
dc.date.available2024-08-05T07:08:59Z
dc.date.issued2022
dc.identifier.doi10.15199/48.2022.04.26
dc.identifier.issn0033-2097
dc.identifier.urihttps://repo.akademiawsei.eu/handle/item/564
dc.languageen
dc.pbn.affiliationinformation and communication technology
dc.relation.ispartofPRZEGLĄD ELEKTROTECHNICZNY
dc.rightsClosedAccess
dc.subject.enEIT reconstruction
dc.subject.enUNetdeep learning
dc.subject.enconvolutional neural networks
dc.titleUNet model in image reconstruction for electrical impedance tomography
dc.typeReviewArticleConference
dspace.entity.typePublication
oaire.citation.issue4
oaire.citation.volume1