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Recently, deep\u2010learning\u2010based image synthesis techniques have attracted considerable attention as alternatives to existing DE methods (i.e., dual\u2010exposure\u2010based and sandwich\u2010detector\u2010based methods) because software\u2010based bone\u2010only and bone\u2010suppression images in CXR could be useful.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The objective of this study was to develop a new framework for DE\u2010like CXR image synthesis from single\u2010energy computed tomography (CT) based on a cycle\u2010consistent generative adversarial network.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The core techniques of the proposed framework are divided into three categories: (1) data configuration from the generation of pseudo CXR from single energy CT, (2) learning of the developed network architecture using pseudo CXR and pseudo\u2010DE imaging using a single\u2010energy CT, and (3) inference of the trained network on real single\u2010energy CXR. We performed a visual inspection and comparative evaluation using various metrics and introduced a figure of image quality (FIQ) to consider the effects of our framework on the spatial resolution and noise in terms of a single index through various test cases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our results indicate that the proposed framework is effective and exhibits potential synthetic imaging ability for two relevant materials: soft tissue and bone structures. Its effectiveness was validated, and its ability to overcome the limitations associated with DE imaging techniques (e.g., increase in exposure dose owing to the requirement of two acquisitions, and emphasis on noise characteristics) via an artificial intelligence technique was presented.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The developed framework addresses X\u2010ray dose issues in the field of radiation imaging and enables pseudo\u2010DE imaging with single exposure.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16329","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T09:54:20Z","timestamp":1677491660000},"page":"1509-1530","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Framework for dual\u2010energy\u2010like chest radiography image synthesis from single\u2010energy computed tomography based on cycle\u2010consistent generative adversarial 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Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaegu","family":"Choi","sequence":"additional","affiliation":[{"name":"Electro\u2010Medical Device Research Center Korea Electrotechnology Research Institute (KERI), Hanggaul\u2010ro, Sangnok\u2010gu  Ansan\u2010si Gyeonggi\u2010do Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo Kyung","family":"Cha","sequence":"additional","affiliation":[{"name":"Electro\u2010Medical Device Research Center Korea Electrotechnology Research Institute (KERI), Hanggaul\u2010ro, Sangnok\u2010gu  Ansan\u2010si Gyeonggi\u2010do Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyuseok","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Integrative Medicine, Major in Digital Healthcare Yonsei University College of Medicine  Gangman\u2010gu Unju\u2010ro Republic of 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