{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T13:45:19Z","timestamp":1792071919416,"version":"4.4.0"},"reference-count":23,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004543","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":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Boron neutron capture therapy (BNCT) is a binary radiotherapy that selectively kills tumor cells. Its clinical implementation relies on Monte Carlo (MC)\u2010based treatment planning systems, which are time\u2010consuming and limit clinical workflow efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To address this limitation, we propose a Multi\u2010Fidelity MC\u2010based Dual\u2010Branch Network (MFMC\u2010DBN) that leverages complementary information from low\u2010 and high\u2010fidelity MC simulations through dual\u2010branch feature fusion, enabling accurate and efficient BNCT dose calculation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The model was trained, validated, and tested using clinical data from 112 glioblastoma patients. Low\u2010statistics coarse and fine mesh MC dose distributions (6.5\u00d710\n                      <jats:sup>6<\/jats:sup>\n                      particle histories) and patient CT information were used to predict three\u2010dimensional dose distributions. Performance was compared with an MC denoising method and a conventional neural network (NN) approach.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>MFMC\u2010DBN outperformed both the NN and MC denoising methods across evaluated metrics. It achieved a mean absolute percentage error (MAPE) of 1.98% in the gross tumor volume (GTV). Organ\u2010specific MAPEs ranged from 1.1% to 3.9%, with a maximum mean absolute error (MAE) of0.28\u00a0Gy (RBE). Gamma analysis further showed high spatial agreement, with passing rates of 99.8% within the patient contour and 99.3% in the GTV under the 3%\/3\u00a0mm criterion. The proposed method maintained high dosimetric accuracy while substantially accelerating BNCT dose calculation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>MFMC\u2010DBN effectively integrates multi\u2010fidelity MC information through a dual\u2010branch attention mechanism, enabling rapid and accurate 3D BNCT dose prediction. By significantly reducing computation time compared with full MC simulations while preserving dosimetric fidelity, these results support the feasibility of the proposed framework for accelerated BNCT dose calculation under a fixed beam configuration.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70646","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T07:32:34Z","timestamp":1787556754000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi\u2010fidelity Monte Carlo fusion for BNCT dose calculation using a dual\u2010branch network"],"prefix":"10.1002","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2380-3972","authenticated-orcid":false,"given":"Leiming","family":"Li","sequence":"first","affiliation":[{"name":"Graduate School of Biomedical Engineering Tohoku University  Sendai Japan"},{"name":"Research Center for Accelerator and Radioisotope Science Tohoku University  Sendai Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Hong","sequence":"additional","affiliation":[{"name":"Graduate School of Biomedical Engineering Tohoku University  Sendai Japan"},{"name":"Research Center for Accelerator and Radioisotope Science Tohoku University  Sendai Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongquan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Nuclear Science and Technology Lanzhou University  Lanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Nuclear Science and Technology Lanzhou University  Lanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Wu","sequence":"additional","affiliation":[{"name":"Graduate School of Biomedical Engineering Tohoku University  Sendai Japan"},{"name":"Research Center for Accelerator and Radioisotope Science Tohoku University  Sendai Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingjie","family":"Xiao","sequence":"additional","affiliation":[{"name":"Graduate School of Biomedical Engineering Tohoku University  Sendai Japan"},{"name":"Research Center for Accelerator and Radioisotope Science Tohoku University  Sendai Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroki","family":"Tanaka","sequence":"additional","affiliation":[{"name":"Division of Particle Radiation Medical Physics Institute for Integrated Radiation and Nuclear Science Kyoto University  Osaka Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroshi","family":"Watabe","sequence":"additional","affiliation":[{"name":"Graduate School of Biomedical Engineering Tohoku University  Sendai Japan"},{"name":"Research Center for Accelerator and Radioisotope Science Tohoku University  Sendai Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10147\u2010019\u201001480\u20104"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/cac2.12089"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.canlet.2008.01.021"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apradiso.2013.11.109"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1089\/cbr.2022.0059"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742\u20106596\/74\/1\/021010"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/55\/12\/016"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apradiso.2011.04.018"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010021\u201001968\u20102"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1080\/00223131.2017.1419890"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.net.2021.11.010"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3025712"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16215"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16966"},{"key":"e_1_2_9_16_1","unstructured":"Shusharina N &Bortfeld T. 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