{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:40:56Z","timestamp":1789105256371,"version":"build-2803163510"},"reference-count":27,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,3,16]],"date-time":"2025-03-16T00:00:00Z","timestamp":1742083200000},"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":["11905295 11975313 11875320 11675042 12375339"],"award-info":[{"award-number":["11905295 11975313 11875320 11675042 12375339"]}],"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":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Magnetic resonance\u2010guided adaptive radiation therapy (MRgART) systems combine Magnetic resonance imaging (MRI) technology with linear accelerators (LINAC) to enhance the precision and efficacy of cancer treatment. These systems enable real\u2010time adjustments of treatment plans based on the latest patient anatomy, creating an urgent need for accurate and rapid dose calculation algorithms. Traditional CT\u2010based dose calculations and ray\u2010tracing (RT) processes are time\u2010consuming and may not be feasible for the online adaptive workflow required in MRgART. Recent advancements in deep learning (DL) offer promising solutions to overcome these limitations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to develop a DL\u2010based dose calculation engine for MRgART that relies solely on MR images. This approach addresses the critical need for accurate and rapid dose calculations in the MRgART workflow without relying on CT images or time\u2010consuming RT processes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We used a deep residual network inspired by U\u2010Net to establish a direct connection between distance\u2010corrected conical (DCC) fluence maps and dose distributions in the image domain. The study utilized data from 30 prostate cancer patients treated with fixed\u2010beam Intensity\u2010Modulated Radiation Therapy (IMRT) on an MR\u2010guided LINAC system. We trained, validated, and tested the model using a total of 120 online treatment plans, which encompassed 1080 individual beams. We extensively evaluated the network's performance by comparing its dose calculation accuracy against Monte Carlo (MC)\u2010based methods using metrics such as mean absolute error (MAE) of pixel\u2010wise dose differences, 3D gamma analysis, dose\u2010volume histograms (DVHs), dosimetric indices, and isodose line similarity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed DL model demonstrated high accuracy in dose calculations. The median MAE of pixel\u2010wise dose differences was 1.2% for the whole body, 1.9% for targets, and 1.1% for organs at risk (OARs). The median 3D gamma passing rates for the 3%\/3\u00a0\u00a0mm criterion were 94.8% for the whole body, 95.7% for targets, and 98.7% for OARs. Additionally, the Dice similarity coefficient (DSC) of isodose lines between the DL\u2010based and MC\u2010based dose calculations averaged 0.94\u00a0\u00b1\u00a00.01. There were no big differences between the DL\u2010based and MC\u2010based calculations in the DVH curves and clinical dosimetric indices. This proved that the two methods were clinically equivalent.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study presents a novel MR\u2010only dose calculation engine that eliminates the need for CT images and complex RT processes. By leveraging DL, the proposed method significantly enhances the efficiency and accuracy of the MRgART workflow, particularly for prostate cancer treatment. This approach holds potential for broader applications across different cancer types and MR\u2010linac systems, paving the way for more streamlined and precise radiation therapy planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17759","type":"journal-article","created":{"date-parts":[[2025,3,16]],"date-time":"2025-03-16T13:20:21Z","timestamp":1742131221000},"page":"4429-4442","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An MR\u2010only deep learning inference model\u2010based dose estimation algorithm for MR\u2010guided adaptive radiation therapy"],"prefix":"10.1002","volume":"52","author":[{"given":"Zhiqiang","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology 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":"Kuo","family":"Men","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology 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":"Weigang","family":"Hu","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"},{"name":"Shanghai Key Laboratory of Radiation Oncology  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianrong","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology 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":"Jiawei","family":"Fan","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"},{"name":"Shanghai Key Laboratory of Radiation Oncology  Shanghai 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