{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T20:31:23Z","timestamp":1792528283097,"version":"4.5.0"},"reference-count":34,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T00:00:00Z","timestamp":1696550400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFC0105311"],"award-info":[{"award-number":["2016YFC0105311"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2019kfyXKJC061"],"award-info":[{"award-number":["2019kfyXKJC061"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cone\u2010beam computed tomography (CBCT) scanning is used for patient setup in image\u2010guided radiotherapy. However, its inaccurate CT numbers limit its applicability in dose calculation and treatment planning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study compares four deep learning methods for generating synthetic CT (sCT) to determine which method is more appropriate and offers potential for further clinical exploration in adaptive proton therapy for nasopharynx cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      CBCTs and deformed planning CT (dCT) from 75 patients (60\/5\/10 for training, validation and testing) were used to compare cycle\u2010consistent Generative Adversarial Network\u00a0(cycleGAN), Unet, Unet+cycleGAN and conditiona\n                      <jats:styled-content>l<\/jats:styled-content>\n                      Generative Adversarial Network (cGAN) for sCT generation. The sCT images generated by each method were evaluated against dCT images using mean absolute error (MAE), structural similarity (SSIM), peak signal\u2010to\u2010noise ratio (PSNR), spatial non\u2010uniformity (SNU) and radial averaging in the frequency domain. In addition, dosimetric accuracy was assessed through gamma analysis, differences in water equivalent thickness (WET), and dose\u2010volume histogram metrics.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The cGAN model has demonstrated optimal performance in the four models across various indicators. In terms of image quality under global condition, the average MAE has been reduced to 16.39HU, SSIM has increased to 95.24%, and PSNR has increased to 28.98. Regarding dosimetric accuracy, the gamma passing rate (2%\/2\u00a0mm) has reached 99.02%, and the WET difference is only 1.28\u00a0mm. The D\n                      <jats:sub>95<\/jats:sub>\n                      value of CTVs coverage and D\n                      <jats:sub>max<\/jats:sub>\n                      value of spinal cord, brainstem show no significant differences between dCT and sCT generated by cGAN model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The cGAN model has been shown to be a more suitable approach for generating sCT using CBCT, considering its characteristics and concepts. The resulting sCT has the potential for application in adaptive proton therapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.16777","type":"journal-article","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T01:04:55Z","timestamp":1696640695000},"page":"6920-6930","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Comparison and evaluation of different deep learning models of synthetic CT generation from CBCT for nasopharynx cancer adaptive proton therapy"],"prefix":"10.1002","volume":"50","author":[{"given":"Bo","family":"Pang","sequence":"first","affiliation":[{"name":"Department of Medical Physics School of Physics and Technology Wuhan University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Si","sequence":"additional","affiliation":[{"name":"Department of Medical Physics School of Physics and Technology Wuhan University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Medical Physics School of Physics and Technology Wuhan University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wensheng","family":"Fu","sequence":"additional","affiliation":[{"name":"Cancer Center Union Hospital Tongji Medical College Huazhong University of Science and Technology  Wuhan China"},{"name":"Hubei Key Laboratory of Precision Radiation Oncology  Wuhan China"},{"name":"Institute of Radiation Oncology Union Hospital Tongji Medical College Huazhong University of Science and Technology  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiling","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Medical Physics School of Physics and Technology Wuhan University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Cancer Center Union Hospital Tongji Medical College Huazhong University of Science and Technology  Wuhan China"},{"name":"Hubei Key Laboratory of Precision Radiation Oncology  Wuhan China"},{"name":"Institute of Radiation Oncology Union Hospital Tongji Medical College Huazhong University of Science and Technology  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Cao","sequence":"additional","affiliation":[{"name":"Cancer Center Union Hospital Tongji Medical College Huazhong University of Science and Technology  Wuhan China"},{"name":"Hubei Key Laboratory of Precision Radiation Oncology  Wuhan China"},{"name":"Institute of Radiation Oncology Union Hospital Tongji Medical College Huazhong University of Science and Technology  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Chang","sequence":"additional","affiliation":[{"name":"Cancer Center Union Hospital Tongji Medical College Huazhong University of Science and Technology  Wuhan China"},{"name":"Hubei Key Laboratory of Precision Radiation Oncology  Wuhan China"},{"name":"Institute of Radiation Oncology Union Hospital Tongji Medical College Huazhong University of Science and Technology  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Quan","sequence":"additional","affiliation":[{"name":"Department of Medical Physics School of Physics and Technology Wuhan University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyong","family":"Yang","sequence":"additional","affiliation":[{"name":"Cancer Center Union Hospital Tongji Medical College Huazhong University of Science and Technology  Wuhan China"},{"name":"Hubei Key Laboratory of Precision Radiation Oncology  Wuhan China"},{"name":"Institute of Radiation Oncology Union Hospital Tongji Medical College Huazhong University of Science and Technology  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,10,6]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.semradonc.2019.02.007"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tipsro.2019.10.003"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.08.019"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2014.12.043"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2013.05.008"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20190594"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2012.02.041"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/56\/15\/019"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1148\/rg.246045065"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.4236\/ojst.2013.35049"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1259\/dmfr\/30642039"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4903260"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.770063"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/54\/21\/016"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4923179"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.semradonc.2019.02.011"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13978"},{"issue":"4","key":"e_1_2_8_19_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_20_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aaf496"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab2770"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13656"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13963"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab22f9"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14121"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14624"},{"key":"e_1_2_8_27_1","doi-asserted-by":"crossref","unstructured":"ZhuJ\u2010Y ParkT IsolaP EfrosA A Unpaired image\u2010to\u2010image translation using cycle\u2010consistent adversarial networks.IEEE International Conference on Computer Vision (ICCV).2017; pp.2242\u20102251.","DOI":"10.1109\/ICCV.2017.244"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1177\/15330338221085358"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac6289"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15020"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13604"},{"key":"e_1_2_8_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2023.100459"},{"key":"e_1_2_8_33_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15930"},{"key":"e_1_2_8_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2019.11.015"},{"key":"e_1_2_8_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2019.09.010"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.16777","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T12:48:04Z","timestamp":1699361284000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.16777"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,6]]},"references-count":34,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["10.1002\/mp.16777"],"URL":"https:\/\/doi.org\/10.1002\/mp.16777","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,6]]},"assertion":[{"value":"2023-06-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-17","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-10-06","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}