{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T07:18:22Z","timestamp":1788419902633,"version":"build-2803163510"},"reference-count":30,"publisher":"IOP Publishing","issue":"17","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Natural Science Foundation of Beijing Municipality","award":["No. Z230003"],"award-info":[{"award-number":["No. Z230003"]}]},{"name":"Postdoctoral Research Funding of Hangzhou International Innovation Institute of Beihang University","award":["No. 2025BKZ101"],"award-info":[{"award-number":["No. 2025BKZ101"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,9,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    In-beam positron emission tomography (PET) provides a promising strategy for dose monitoring in carbon ion radiotherapy (CIRT), but accurate dose prediction remains difficult due to the complex, nonlinear relationship between positron-emitter activity and physical dose deposition. This proof-of-concept study aimed to improve activity-to-dose mapping by developing decomposition-based deep learning frameworks with auxiliary physical supervision.\n                    <jats:italic>Approach.<\/jats:italic>\n                    Idealized Monte Carlo (MC) simulations were conducted on computed tomography (CT) phantoms from 18 non-small cell lung cancer patients. The models were designed to predict laterally integrated one-dimensional depth-dose distributions for individual pencil-beam spots from corresponding 5 min cumulative activity and CT Hounsfield unit profiles. Two decomposition-based models, TemcoNet and NucoNet, incorporated Transformer-based decomposition modules supervised by cumulative post-irradiation activity at 10, 15, and 20 min and nuclide-specific yields of\n                    <jats:sup>11<\/jats:sup>\n                    C,\n                    <jats:sup>15<\/jats:sup>\n                    O, and\n                    <jats:sup>10<\/jats:sup>\n                    C, respectively, while DirectNet served as a baseline.\n                    <jats:italic>Main results.<\/jats:italic>\n                    Compared with MC ground truth, all models achieved similar median range accuracy, but TemcoNet and NucoNet substantially improved dose prediction, reducing the mean relative error from 2.36% for DirectNet to below 0.4%. The mean gamma passing rate at 2 mm\/2% increased from 45.31% to approximately 96% for both decomposition-based models. Ablation experiments showed that the decomposition pathway learned physically meaningful intermediate representations, and that nuclide-yield supervision provided an additional dose-prediction benefit.\n                    <jats:italic>Significance.<\/jats:italic>\n                    Physics-informed decomposition-based modeling improves MC-derived positron-emitter activity-to-dose mapping by combining effective representation learning with auxiliary physical supervision. The proposed framework improves dose prediction while incorporating physically meaningful priors into the learning process, offering a promising basis for PET-based dose-monitoring model development in CIRT.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae9d0f","type":"journal-article","created":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T22:53:58Z","timestamp":1787352838000},"page":"175022","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A physics-informed decomposition network for carbon ion radiotherapy dose monitoring: a proof-of-concept study"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5245-903X","authenticated-orcid":true,"given":"Xin-Yu","family":"Hu","sequence":"first","affiliation":[{"name":"Beihang University"},{"name":"CAS Ion Medical Technology Co., Ltd"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[{"name":"Beihang University"},{"name":"CAS Ion Medical Technology Co., Ltd"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Guang","family":"Li","sequence":"additional","affiliation":[{"name":"Beihang University"},{"name":"CAS Ion Medical Technology Co., Ltd"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6654-3644","authenticated-orcid":true,"given":"Yu-Ying","family":"Yin","sequence":"additional","affiliation":[{"name":"Beihang University"},{"name":"CAS Ion Medical Technology Co., Ltd"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Yang","sequence":"additional","affiliation":[{"name":"CAS Ion Medical Technology Co., Ltd"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Chang","sequence":"additional","affiliation":[{"name":"CAS Ion Medical Technology Co., Ltd"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2425-510X","authenticated-orcid":true,"given":"Ming-Qing","family":"Wang","sequence":"additional","affiliation":[{"name":"Cancer Center, Peking University Third Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7891-0105","authenticated-orcid":true,"given":"Kai-Wen","family":"Li","sequence":"additional","affiliation":[{"name":"CAS Ion Medical Technology Co., Ltd"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5382-2751","authenticated-orcid":true,"given":"Xueying","family":"Yang","sequence":"additional","affiliation":[{"name":"Beihang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5626-0704","authenticated-orcid":true,"given":"Li-Sheng","family":"Geng","sequence":"additional","affiliation":[{"name":"Beihang University"},{"name":"Beihang University"},{"name":"Beihang University"},{"name":"Beihang University"},{"name":"Chinese Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"pmbae9d0fbib1","doi-asserted-by":"publisher","DOI":"10.7937\/K9\/TCIA.2015.PF0M9REI","type":"dataset","article-title":"Data from NSCLC-Radiomics (version 4)","author":"Aerts","year":"2014","unstructured":"Aerts H J W L 2014 Data from NSCLC-Radiomics (version 4) The Cancer Imaging Archive 10.7937\/K9\/TCIA.2015.PF0M9REI"},{"key":"pmbae9d0fbib2","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ad4f48","type":"journal-article","article-title":"A quantitative assessment of Geant4 for predicting the yield and distribution of positron-emitting fragments in ion beam therapy","volume":"69","author":"Chacon","year":"2024","journal-title":"Phys. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-04-16","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-08-21","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-03","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}