{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T14:12:10Z","timestamp":1785852730473,"version":"3.56.0"},"reference-count":34,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T00:00:00Z","timestamp":1666051200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Cone\u2010beam computed tomography (CBCT) is widely used for daily image guidance in radiation therapy, enhancing the reproducibility of patient setup. However, its application in adaptive radiotherapy (ART) is limited by many imaging artifacts and inaccurate Hounsfield units (HUs). The correction of CBCT image is necessary and of great value for CBCT\u2010based ART.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To explore the synthetic CT (sCT) generation from CBCT images of thorax and abdomen patients, which usually surfer from serious artifacts duo to organ state changes. In this study, a streaking artifact reduction network (SARN) is proposed to reduce artifacts and combine with cycleGAN to generate high\u2010quality sCT images from CBCT and achieve an accurate dose calculation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The proposed SARN was trained in a self\u2010supervised manner. Artifact\u2010CT images were generated from planning CT by random deformation and projection replacement, and SARN was trained based on paired artifact\u2014CT and CT images. The planning CT and CBCT images of 260 patients with cancer, including 120 thoracic and 140 abdominal CT scans, were used to train and evaluate neural networks. The CBCT images of another 12 patients in late treatment fractions, which contained large anatomy changes, were also tested by trained models. The trained models include commonly used U\u2010Net, cycleGAN, attention\u2010gated cycleGAN (cycAT), and cascade models combined SARN with cycleGAN or cycAT. The generated sCT images were compared in terms of image quality and dose calculation accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The sCT images generated by SARN combined with cycleGAN and cycAT showed the best image quality, removed the most artifacts, and retained the normal anatomical structure. The SARN+cycleGAN performed best in streaking artifacts removal with the maximum percent integrity uniformity (PIU<jats:sub>m<\/jats:sub>) of 91.0% and minimum standard deviation (SD) of 35.4 HU for delineated artifact regions among all models. The mean absolute error (MAE) of CBCT images in the thorax and abdomen were 71.6 and 55.2 HU, respectively, using planning CT images after deformable registration as ground truth. Compared with CBCT, the thoracic and abdominal sCT images generated by each model had significantly improved image quality with smaller MAE (<jats:italic>p<\/jats:italic>&lt; 0.05). The SARN+cycAT obtained the minimum MAEs of 42.5 HU in the thorax while SARN+cycleGAN got the minimum MAEs of 32.0 HU in the abdomen. The sCT generated by U\u2010Net had a remarkably lower anatomical structure accuracy compared with the other models. The thoracic and abdominal sCT images generated by SARN+cycleGAN showed optimal dose calculation accuracy with gamma passing rates (2 mm\/2%) of 98.2% and 96.9%, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed SARN can reduce serious streaking artifacts in CBCT images. The SARN combined with cycleGAN can generate high\u2010quality sCT images with fewer artifacts, high\u2010accuracy HU values, and accurate anatomical structures, thus providing reliable dose calculation in ART.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16017","type":"journal-article","created":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T09:30:15Z","timestamp":1664703015000},"page":"879-893","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Streaking artifact reduction for CBCT\u2010based synthetic CT generation in adaptive radiotherapy"],"prefix":"10.1002","volume":"50","author":[{"given":"Liugang","family":"Gao","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering Southeast University Nanjing China"},{"name":"The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University Changzhou China"},{"name":"Jiangsu Province Engineering Research Center of Medical Physics Changzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Xie","sequence":"additional","affiliation":[{"name":"The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University Changzhou China"},{"name":"Jiangsu Province Engineering Research Center of Medical Physics Changzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Sun","sequence":"additional","affiliation":[{"name":"The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University Changzhou China"},{"name":"Jiangsu Province Engineering Research Center of Medical Physics Changzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Lin","sequence":"additional","affiliation":[{"name":"The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University Changzhou China"},{"name":"Jiangsu Province Engineering Research Center of Medical Physics Changzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfeng","family":"Sui","sequence":"additional","affiliation":[{"name":"The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University Changzhou China"},{"name":"Jiangsu Province Engineering Research Center of Medical Physics Changzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanyu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Southeast University Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinye","family":"Ni","sequence":"additional","affiliation":[{"name":"The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University Changzhou China"},{"name":"Jiangsu Province Engineering Research Center of Medical Physics Changzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,10,18]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2003.12.024"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/42\/1\/008"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.semradonc.2019.02.007"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/40\/11\/007"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/54\/21\/016"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1786171"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4923179"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2016.11.001"},{"issue":"4","key":"e_1_2_8_10_1","article-title":"Cone beam computed tomography image quality improvement using a deep convolutional neural network","volume":"10","author":"Kida S","year":"2018","journal-title":"Cureus"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13175"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15150"},{"key":"e_1_2_8_13_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT.U\u2010Net: Convolutional networks for biomedical image segmentation. 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