{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:57:42Z","timestamp":1764784662068,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T00:00:00Z","timestamp":1622678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2020GFZD008 and 2020GFYD011"],"award-info":[{"award-number":["2020GFZD008 and 2020GFYD011"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51607029 and 61836011"],"award-info":[{"award-number":["51607029 and 61836011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The 111 Project","award":["B16009"],"award-info":[{"award-number":["B16009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Image reconstruction of Magnetic induction tomography (MIT) is an ill-posed problem. The non-linear characteristics lead many difficulties to its solution. In this paper, a method based on a Generative Adversarial Network (GAN) is presented to tackle these barriers. Firstly, the principle of MIT is analyzed. Then the process for finding the global optimum of conductivity distribution is described as a training process, and the GAN model is proposed. Finally, the image was reconstructed by a part of the model (the generator). All datasets are obtained from an eight-channel MIT model by COMSOL Multiphysics software. The voltage measurement samples are used as input to the trained network, and its output is an estimate for image reconstruction of the internal conductivity distribution. The results based on the proposed model and the traditional algorithms were compared, which have shown that average root mean squared error of reconstruction results obtained by the proposed method is 0.090, and the average correlation coefficient with original images is 0.940, better than corresponding indicators of BPNN and Tikhonov regularization algorithms. Accordingly, the GAN algorithm was able to fit the non-linear relationship between input and output, and visual images also show that it solved the usual problems of artifact in traditional algorithm and hot pixels in L2 regularization, which is of great significance for other ill-posed or non-linear problems.<\/jats:p>","DOI":"10.3390\/s21113869","type":"journal-article","created":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T21:03:32Z","timestamp":1622754212000},"page":"3869","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Application of a Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9790-0851","authenticated-orcid":false,"given":"Dan","family":"Yang","sequence":"first","affiliation":[{"name":"Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang 110819, China"},{"name":"Key Laboratory of Infrared Optoelectric Materials and Micro-Nano Devices, Shenyang 110819, China"},{"name":"College of Information Science and Engineering, Northeastern University, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahua","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang 110819, China"},{"name":"Key Laboratory of Infrared Optoelectric Materials and Micro-Nano Devices, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuchen","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared Optoelectric Materials and Micro-Nano Devices, Shenyang 110819, China"},{"name":"College of Information Science and Engineering, Northeastern University, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northeastern University, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3324","DOI":"10.1109\/TIM.2012.2205516","article-title":"Theoretical and Experimental Evaluation of Rotational Magnetic Induction Tomography","volume":"61","author":"Wei","year":"2012","journal-title":"IEEE Trans. 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