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Med. Biol."],"published-print":{"date-parts":[[2026,7,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Purpose.<\/jats:italic>\n                    Accurate verification of delivered dose requires anatomical information during the treatment beam delivery. However, conventional cone-beam computed tomography (CBCT) is typically acquired prior to beam delivery and fails to account for intrafractional tumor shift\/drift. This study aims to develop a 4D on-beam computed tomography (4D OBCT) framework to capture anatomical states during irradiation.\n                    <jats:italic>Materials and methods.<\/jats:italic>\n                    A thoracic phantom with rigid tumor motion was used to develop a conformal arc treatment plan. During delivery, the target was displaced by 1 cm superior to its planned position to simulate a baseline shift, and megavoltage transmission data were acquired using an electronic portal imaging device. Transmission data corresponding to the planned tumor position were obtained from the planning CT (pCT) via Monte Carlo simulation. Volumetric difference images were reconstructed from the differences between the measured and simulated transmission data for each phase. Subsequently, the images were superimposed onto the corresponding phases of the 4D pCT to obtain 4D OBCT. For evaluation, we compared 4D OBCT with 4D pCT (ground-truth) and 4D CBCT which were acquired after the target displacement. A dosimetric performance was evaluated by recalculating 4D dose distributions on the three images and comparing in dose-volume histograms and gamma indices.\n                    <jats:italic>Results.<\/jats:italic>\n                    4D OBCT achieved lower root mean square error than 4D CBCT in both the lung (58.46 HU vs 74.37 HU) and chest wall regions (23.78 HU vs 66.84 HU). This quantitative accuracy was also reflected in the dosimetric comparison. Under 2%\/2 mm and 1%\/1 mm criteria, 4D OBCT achieved gamma pass rates of 99.76% and 99.08%, respectively, whereas 4D CBCT yielded 72.50% and 57.77%, respectively.\n                    <jats:italic>Conclusions.<\/jats:italic>\n                    The proposed 4D OBCT framework successfully reconstructed accurate phase-resolved images of the target during irradiation. By providing high-resolution volumetric images that reflect intrafractional motion, 4D OBCT could provide a robust solution for precise post-delivery dosimetric verification and adaptation.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae8355","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T22:54:29Z","timestamp":1782514469000},"page":"135034","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Four-dimensional on-beam computed tomography reconstruction using projection-difference images"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7746-2433","authenticated-orcid":true,"given":"Joonil","family":"Hwang","sequence":"first","affiliation":[{"name":"Yonsei University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6027-9034","authenticated-orcid":true,"given":"Raymond Hyunwoo","family":"Moon","sequence":"additional","affiliation":[{"name":"Korea Advanced Institute of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jihyung","family":"Yoon","sequence":"additional","affiliation":[{"name":"University of Rochester Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kwang-Ho","family":"Cheong","sequence":"additional","affiliation":[{"name":"Hanllym University Sacred Heart Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Won","family":"Jung","sequence":"additional","affiliation":[{"name":"East Carolina University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9409-3628","authenticated-orcid":true,"given":"Seungryong","family":"Cho","sequence":"additional","affiliation":[{"name":"Korea Advanced Institute of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inhwan","family":"Yeo","sequence":"additional","affiliation":[{"name":"INOVA Schar Cancer Institute"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1165-1509","authenticated-orcid":true,"given":"Hoyeon","family":"Lee","sequence":"additional","affiliation":[{"name":"University of Hong Kong"},{"name":"University of Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"pmbae8355bib1","doi-asserted-by":"publisher","first-page":"5593","DOI":"10.1088\/0031-9155\/58\/16\/5593","type":"journal-article","article-title":"Geant4-based Monte Carlo simulations on GPU for medical applications","volume":"58","author":"Bert","year":"2013","journal-title":"Phys. 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