{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T02:19:25Z","timestamp":1787797165822,"version":"build-2784847793"},"reference-count":28,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T00:00:00Z","timestamp":1672444800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"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":["12205376"],"award-info":[{"award-number":["12205376"]}],"id":[{"id":"10.13039\/501100001809","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":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Respiration has a major impact on the accuracy of radiation treatment for thorax and abdominal tumours. Instantaneous volumetric imaging could provide precise knowledge of tumour and normal organs\u2019 three\u2010dimensional (3D) movement, which is the key to reducing the negative effect of breathing motion. Therefore, this study proposed a real\u2010time 3D MRI reconstruction method from cine\u2010MRI using an unsupervised network.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and materials<\/jats:title>\n                    <jats:p>Cine\u2010MRI and setup 3D\u2010MRI from eight patients with liver cancer were utilized to establish and validate the deep learning network for 3D\u2010MRI reconstruction. Unlike previous methods that required 4D\u2010MRI for network training, the proposed method utilized a reference 3D\u2010MRI and cine\u2010MRI to generate the training data. Then, a network was trained in an unsupervised manner to estimate the relationship between the cine\u2010MRI acquired on coronal plane and deformation vector field (DVF) that describes the patient's breathing motion. After the training process, the coronal cine\u2010MRI were inputted into the network, and the corresponding DVF was obtained. By wrapping the reference 3D\u2010MRI with the generated DVF, the 3D\u2010MRI could be reconstructed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The reconstructed 3D\u2010MRI slices were compared with the corresponding phase\u2010sorted cine\u2010MRI using dice similarity coefficients (DSCs) of liver contours and blood vessel localization error. In all patients, the liver DSC had mean value &gt;96.1% and standard deviation\u00a0&lt;\u00a01.3%; the blood vessel localization error had mean value &lt;2.6\u00a0mm, and standard deviation was &lt;2.0\u00a0mm. Moreover, the time for 3D\u2010MRI reconstruction was approximately 100\u00a0ms. These results indicated that the proposed method could accurately reconstruct the 3D\u2010MRI in real time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed method could accurately reconstruct the 3D\u2010MRI from cine\u2010MRI in real time. This method has great potential in improving the accuracy of radiotherapy for moving tumours.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.16141","type":"journal-article","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T01:13:57Z","timestamp":1670894037000},"page":"3584-3596","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Real\u2010time 3D MRI reconstruction from cine\u2010MRI using unsupervised network in MRI\u2010guided radiotherapy for liver cancer"],"prefix":"10.1002","volume":"50","author":[{"given":"Ran","family":"Wei","sequence":"first","affiliation":[{"name":"National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College  Beijing China"},{"name":"National Cancer Center\/National Clinical Research Center for Cancer\/Hebei Cancer Hospital Chinese Academy of Medical Sciences  Langfang 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Cancer\/Hebei Cancer Hospital Chinese Academy of Medical Sciences  Langfang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuo","family":"Men","sequence":"additional","affiliation":[{"name":"National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianrong","family":"Dai","sequence":"additional","affiliation":[{"name":"National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College  Beijing 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