{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T21:52:13Z","timestamp":1790545933469,"version":"4.1.0"},"reference-count":30,"publisher":"Elsevier BV","issue":"4","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100005090","name":"Beijing Nova Program","doi-asserted-by":"publisher","award":["Z201100006820058"],"award-info":[{"award-number":["Z201100006820058"]}],"id":[{"id":"10.13039\/501100005090","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11975313"],"award-info":[{"award-number":["11975313"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12005302"],"award-info":[{"award-number":["12005302"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12175312"],"award-info":[{"award-number":["12175312"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","redjournal.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Radiation Oncology*Biology*Physics"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1016\/j.ijrobp.2022.03.032","type":"journal-article","created":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T02:13:47Z","timestamp":1650507227000},"page":"885-892","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":12,"title":["Personalized Modeling to Improve Pseudo\u2013Computed Tomography Images for Magnetic Resonance Imaging\u2013Guided Adaptive Radiation Therapy"],"prefix":"10.1016","volume":"113","author":[{"given":"Xiangyu","family":"Ma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyuan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shirui","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuena","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianrong","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuo","family":"Men","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ijrobp.2022.03.032_bib0001","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20160667","article-title":"The future of image-guided radiotherapy will be MR guided","volume":"90","author":"Pollard","year":"2017","journal-title":"Br J Radio"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0002","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1186\/s13014-020-01524-4","article-title":"Medical physics challenges in clinical MR-guided radiotherapy","volume":"15","author":"Kurz","year":"2020","journal-title":"Radiat Oncol"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0003","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2020.616291","article-title":"MR-guided radiotherapy for prostate cancer","volume":"10","author":"Tocco","year":"2020","journal-title":"Front Oncol"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0004","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.radonc.2020.10.018","article-title":"A deep learning approach to generate synthetic CT in low field MR-guided adaptive radiotherapy for abdominal and pelvic cases","volume":"153","author":"Cusumano","year":"2020","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0005","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s11547-019-01090-0","article-title":"On the accuracy of bulk synthetic CT for MR-guided online adaptive radiotherapy","volume":"125","author":"Cusumano","year":"2020","journal-title":"Radiol Med"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0006","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.media.2018.03.011","article-title":"Deep embedding convolutional neural network for synthesizing ct image from T1-weighted MR image","volume":"47","author":"Xiang","year":"2018","journal-title":"Med Image Anal"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0007","doi-asserted-by":"crossref","first-page":"4095","DOI":"10.1002\/mp.13663","article-title":"Dosimetric evaluation of synthetic ct for head and neck radiotherapy generated by a patch-based three-dimensional convolutional neural network","volume":"46","author":"Dinkla","year":"2019","journal-title":"Med Phys"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0008","doi-asserted-by":"crossref","first-page":"5218","DOI":"10.1002\/mp.13187","article-title":"Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI-guided radiation planning in the pelvic region","volume":"45","author":"Arabi","year":"2018","journal-title":"Med Phys"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0009","doi-asserted-by":"crossref","first-page":"5659","DOI":"10.1002\/mp.13247","article-title":"Technical note: U-net-generated synthetic CT images for magnetic resonance imaging-only prostate intensity-modulated radiation therapy treatment planning","volume":"45","author":"Chen","year":"2018","journal-title":"Med Phys"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0010","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/j.ijrobp.2018.05.058","article-title":"MR-only brain radiation therapy: Dosimetric evaluation of synthetic CTs generated by a dilated convolutional neural network","volume":"102","author":"Dinkla","year":"2018","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0011","doi-asserted-by":"crossref","DOI":"10.1088\/2057-1976\/ab6e1f","article-title":"Generation of abdominal synthetic cts from 0.35t MR images using generative adversarial networks for MR-only liver radiotherapy","volume":"6","author":"Fu","year":"2020","journal-title":"Biomed Phys Eng Express"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0012","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/aada6d","article-title":"Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy","volume":"63","author":"Maspero","year":"2018","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0013","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1016\/j.ijrobp.2019.08.049","article-title":"Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning","volume":"105","author":"Largent","year":"2019","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0014","doi-asserted-by":"crossref","first-page":"4135","DOI":"10.1002\/mp.13716","article-title":"Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR-only breast radiotherapy","volume":"46","author":"Olberg","year":"2019","journal-title":"Med Phys"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0015","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1093\/jrr\/rrz063","article-title":"Feasibility of synthetic computed tomography generated with an adversarial network for multi-sequence magnetic resonance-based brain radiotherapy","volume":"61","author":"Koike","year":"2020","journal-title":"J Radiat Res"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0016","doi-asserted-by":"crossref","first-page":"1880","DOI":"10.1002\/mp.14075","article-title":"Multi-sequence MR image-based synthetic CT generation using a generative adversarial network for head and neck MRI-only radiotherapy","volume":"47","author":"Qi","year":"2020","journal-title":"Med Phys"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0017","doi-asserted-by":"crossref","first-page":"1750","DOI":"10.1002\/mp.14062","article-title":"Pseudo-CT generation from multi-parametric MRI using a novel multi-channel multi-path conditional generative adversarial network for nasopharyngeal carcinoma patients","volume":"47","author":"Tie","year":"2020","journal-title":"Med Phys"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0018","article-title":"MRI-based treatment planning for proton radiotherapy: Dosimetric validation of a deep learning-based liver synthetic CT generation method","volume":"64","author":"Liu","year":"2019","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0019","doi-asserted-by":"crossref","first-page":"12","DOI":"10.14338\/IJPT-19-00062.1","article-title":"MRI-based proton treatment planning for base of skull tumors","volume":"6","author":"Shafai-Erfani","year":"2019","journal-title":"Int J Part Ther"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0020","first-page":"66","article-title":"Abdominal synthetic CT reconstruction with intensity projection prior for MRI-only adaptive radiotherapy","author":"Olberg","year":"2021","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0021","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.radonc.2020.06.049","article-title":"Magnetic resonance-based synthetic computed tomography images generated using generative adversarial networks for nasopharyngeal carcinoma radiotherapy treatment planning","volume":"150","author":"Peng","year":"2020","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0022","series-title":"Paper presented at: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","article-title":"Memory matching networks for one-shot image recognition","author":"Ca","year":"2018"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101793","article-title":"Cascaded one-shot deformable convolutional neural networks: Developing a deep learning model for respiratory motion estimation in ultrasound sequences","volume":"65","author":"Liu","year":"2020","journal-title":"Med Image Anal"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0024","doi-asserted-by":"crossref","first-page":"3968","DOI":"10.1002\/mp.14898","article-title":"Automatic clinical target volume delineation for cervical cancer in CT images using deep learning","volume":"48","author":"Shi","year":"2021","journal-title":"Med Phys"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0025","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2021.713617","article-title":"MRI-only radiotherapy planning for nasopharyngeal carcinoma using deep learning","volume":"11","author":"Ma","year":"2021","journal-title":"Front Oncol"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0026","series-title":"IEEE Conference on Computer Vision & Pattern Recognition","article-title":"Image-to-image translation with conditional adversarial networks","author":"Isola","year":"2016"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0027","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.radonc.2018.10.037","article-title":"Segmenting lung tumors on longitudinal imaging studies via a patient-specific adaptive convolutional neural network","volume":"131","author":"Wang","year":"2019","journal-title":"Radiothe Oncol"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0028","doi-asserted-by":"crossref","first-page":"3003","DOI":"10.1002\/mp.14866","article-title":"Improving generalization in MR-to-CT synthesis in radiotherapy by using an augmented cycle generative adversarial network with unpaired data","volume":"48","author":"Brou Boni","year":"2021","journal-title":"Med Phys"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0029","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.1002\/mrm.27134","article-title":"Zero TE-based pseudo-CT image conversion in the head and its application in PET\/MR attenuation correction and MR-guided radiation therapy planning","volume":"80","author":"Wiesinger","year":"2018","journal-title":"Magn Reson Med"},{"key":"10.1016\/j.ijrobp.2022.03.032_bib0030","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1002\/mp.13927","article-title":"Patch-based generative adversarial neural network models for head and neck MR-only planning","volume":"47","author":"Klages","year":"2020","journal-title":"Med Phys"}],"container-title":["International Journal of Radiation Oncology*Biology*Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0360301622002784?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0360301622002784?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T10:00:35Z","timestamp":1759744835000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0360301622002784"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":30,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["S0360301622002784"],"URL":"https:\/\/doi.org\/10.1016\/j.ijrobp.2022.03.032","relation":{},"ISSN":["0360-3016"],"issn-type":[{"value":"0360-3016","type":"print"}],"subject":[],"published":{"date-parts":[[2022,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Personalized Modeling to Improve Pseudo\u2013Computed Tomography Images for Magnetic Resonance Imaging\u2013Guided Adaptive Radiation Therapy","name":"articletitle","label":"Article Title"},{"value":"International Journal of Radiation Oncology*Biology*Physics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ijrobp.2022.03.032","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}