{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T17:30:31Z","timestamp":1778175031273,"version":"3.51.4"},"reference-count":32,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T00:00:00Z","timestamp":1747785600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12375339"],"award-info":[{"award-number":["12375339"]}],"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":["11675042"],"award-info":[{"award-number":["11675042"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC2404603"],"award-info":[{"award-number":["2022YFC2404603"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC2404600"],"award-info":[{"award-number":["2022YFC2404600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFC2418400"],"award-info":[{"award-number":["2024YFC2418400"]}],"id":[{"id":"10.13039\/501100012166","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":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Modern intensity\u2010modulated radiotherapy, aiming to deliver an accurate dose to the planning target volume while protecting the surrounding organs at risk, is regarded as the indispensable treatment for cancer in the clinic. An accurate and efficient dose calculation algorithm is essential to support and accelerate the treatment plan optimization process.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>To improve the effectiveness of the dose calculation, deep learning methods are widely adopted to compute dose distributions from several variables mimicking the physical features required for dose calculation. However, these methods employed similar concepts and neural network inputs, and had only limited improvements to traditional dose calculation methods. In this work, we propose a new computing process, named DeepBEVdose, which is essentially distinct to the previous deep learning\u2010based dose calculation methods. We present a novel image\u2010domain dose calculation algorithm to automatically compute dose distributions from the computer tomography images and radiation field fluence maps. Specifically, a novel beam's eye view calculation scheme is introduced to substitute the traditional trivial ray\u2010tracing procedure that cannot be removed in the previously published dose calculation system. Under this new calculation scheme, a generic two\u2010dimensional convolutional neural network with minimal input requirements is sufficient to perform accurate dose calculation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We demonstrate the feasibility of this approach with datasets acquired from multi\u2010institutions with two different tumor sites (nasopharynx and lung). The average pixel\u2010wise difference between the ground truth and the predicted results across all testing cases (both internal and external) is within 2%. The average 3D gamma passing rate is above 95% for all testing cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We have developed a novel deep learning framework that effectively maps radiation fluences to dose distributions. The high accuracy and efficiency of the proposed approach indicate its potential for use in online adaptive plan optimization.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17903","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T00:43:37Z","timestamp":1747874617000},"page":"5039-5050","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A novel dose calculation system implemented in image domain"],"prefix":"10.1002","volume":"52","author":[{"given":"Jiawei","family":"Fan","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Fudan University Shanghai Cancer Center  Shanghai China"},{"name":"Department of Oncology Shanghai Medical College Fudan University  Shanghai China"},{"name":"Shanghai Clinical Research Center for Radiation Oncology  Shanghai China"},{"name":"Shanghai Key Laboratory of Radiation Oncology  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoling","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Fudan University Shanghai Cancer Center  Shanghai China"},{"name":"Department of Oncology Shanghai Medical College Fudan University  Shanghai China"},{"name":"Shanghai Clinical Research Center for Radiation Oncology  Shanghai China"},{"name":"Shanghai Key Laboratory of Radiation Oncology  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiazhou","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Fudan University Shanghai Cancer Center  Shanghai China"},{"name":"Department of Oncology Shanghai Medical College Fudan University  Shanghai China"},{"name":"Shanghai Clinical Research Center for Radiation Oncology  Shanghai China"},{"name":"Shanghai Key Laboratory of Radiation Oncology  Shanghai 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