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dc.contributor.authorHuang, Shu-Weien_US
dc.contributor.authorCheng, Hao-Minen_US
dc.contributor.authorLin, Shien-Fongen_US
dc.date.accessioned2020-10-05T02:02:23Z-
dc.date.available2020-10-05T02:02:23Z-
dc.date.issued2019-01-01en_US
dc.identifier.isbn978-1-5386-1311-5en_US
dc.identifier.issn1557-170Xen_US
dc.identifier.urihttp://hdl.handle.net/11536/155546-
dc.description.abstractElectrical impedance tomography (EIT) is a noninvasive and non-radiative medical imaging technique based on detecting the inhomogeneous electrical properties of the tissue. The inverse problem of EIT is a highly nonlinear ill-posed problem, which is the main reason that affects image quality. Our goal is to solve the EIT inverse problem using the nonlinear mapping properties of artificial neural networks (ANNs) and convolutional neural networks (CNNs). In this paper, the adaptive moment estimation (ADAM) optimization method and mean-square-error (MSE) function are used to train an ANN to solve the inverse problem and a CNN to process the ANN image. The networks are trained on datasets of simulated data, and tested on datasets of simulated data and experimental data. Results for time-difference EIT (td-EIT) images are presented using simulated EIT data from EIDORS and experimental EIT data from our EIT systems. The results are used to compare the proposed method with the one-step Gauss-Newton linear method and RBFNN method. The proposed method offers improved resolution (RES), low position error (PE) and excellent artefact removal compared to the existing methods. The experimental results show that our method can improve the RES by 50 to 70 percent and reduce the PE by 60 to 70 percent. The improvements in RES and processing speed are essential for clinical EIT measurement of dynamic physiological processes.en_US
dc.language.isoen_USen_US
dc.subjectElectrical impedance tomographyen_US
dc.subjectConductivity imagingen_US
dc.subjectInverse solveen_US
dc.subjectArtificial neural networken_US
dc.titleImproved Imaging Resolution of Electrical Impedance Tomography Using Artificial Neural Networks for Image Reconstructionen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)en_US
dc.citation.spage1551en_US
dc.citation.epage1554en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.identifier.wosnumberWOS:000557295301228en_US
dc.citation.woscount0en_US
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