{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:14:01Z","timestamp":1753888441971,"version":"3.41.2"},"reference-count":37,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T00:00:00Z","timestamp":1619395200000},"content-version":"vor","delay-in-days":115,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772241"],"award-info":[{"award-number":["61772241"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p><jats:italic>Background<\/jats:italic>. The generation of medical images is to convert the existing medical images into one or more required medical images to reduce the time required for sample diagnosis and the radiation to the human body from multiple medical images taken. Therefore, the research on the generation of medical images has important clinical significance. At present, there are many methods in this field. For example, in the image generation process based on the fuzzy C\u2010means (FCM) clustering method, due to the unique clustering idea of FCM, the images generated by this method are uncertain of the attribution of certain organizations. This will cause the details of the image to be unclear, and the resulting image quality is not high. With the development of the generative adversarial network (GAN) model, many improved methods based on the deep GAN model were born. Pix2Pix is a GAN model based on UNet. The core idea of this method is to use paired two types of medical images for deep neural network fitting, thereby generating high\u2010quality images. The disadvantage is that the requirements for data are very strict, and the two types of medical images must be paired one by one. DualGAN model is a network model based on transfer learning. The model cuts the 3D image into multiple 2D slices, simulates each slice, and merges the generated results. The disadvantage is that every time an image is generated, bar\u2010shaped \u201cshadows\u201d will be generated in the three\u2010dimensional image. <jats:italic>Method\/Material<\/jats:italic>. To solve the above problems and ensure the quality of image generation, this paper proposes a Dual3D&amp;PatchGAN model based on transfer learning. Since Dual3D&amp;PatchGAN is set based on transfer learning, there is no need for one\u2010to\u2010one paired data sets, only two types of medical image data sets are needed, which has important practical significance for applications. This model can eliminate the bar\u2010shaped \u201cshadows\u201d produced by DualGAN\u2019s generated images and can also perform two\u2010way conversion of the two types of images. <jats:italic>Results<\/jats:italic>. From the multiple evaluation indicators of the experimental results, it can be analyzed that Dual3D&amp;PatchGAN is more suitable for the generation of medical images than other models, and its generation effect is better.<\/jats:p>","DOI":"10.1155\/2021\/9979606","type":"journal-article","created":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T19:26:49Z","timestamp":1619465209000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Transfer Deep Generative Adversarial Network Model to Synthetic Brain CT Generation from MR Images"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7962-9466","authenticated-orcid":false,"given":"Yi","family":"Gu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9392-2976","authenticated-orcid":false,"given":"Qiankun","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,4,26]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/tia.2020.3032932"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1177\/0165551519837176"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22272"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1175\/JCLI-D-18-0718.1"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2990611"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.3023279"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2020.3000593"},{"volume-title":"Generative Adversarial Nets. 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