文章基本信息

标题：Image Reconstruction for Electrical Impedance Tomography: Experimental Comparison of Radial Basis Neural Network and Gauss – Newton Method
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作者：Radek Hrabuska ; Michal Prauzek ; Marketa Venclikova 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：6
页码：438-443
DOI：10.1016/j.ifacol.2018.07.114
语种：English
出版社：Elsevier
摘要：AbstractElectrical impedance tomography (EIT) is an intensively researched noninvasive diagnostic method for medical use, that can help to improve the lung diagnostics, artificial lung ventilation and prevent lung injuries. Further improvements of reconstruction algorithms and measurement devices are essential to widen the use of EIT as a lung diagnostic method. To test potential of Radial Basis Neural Networks (RBNN) and Hopfield Neural Networks (HNN) for image reconstruction experiment is carried. Said neural networks are compared with Gauss – Newton (GN) algorithm. Results of the experiment show higher reconstruction accuracy with RBNN and HNN over GN algorithm.
关键词：KeywordsElectrical Impedance TomographyNeural NetworksImage Reconstruction Algorithms