{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:07:19Z","timestamp":1791392839067,"version":"4.3.3"},"reference-count":88,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T00:00:00Z","timestamp":1659916800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2022,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Megavoltage computed tomography (MVCT) has been implemented on many radiotherapy treatment machines for on\u2010board anatomical visualization, localization, and adaptive dose calculation. Implementing an MR\u2010only workflow by synthesizing MVCT from magnetic resonance imaging (MRI) would offer numerous advantages for treatment planning and online adaptation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>In this work, we sought to synthesize MVCT (sMVCT) datasets from MRI using deep learning to demonstrate the feasibility of MRI\u2010MVCT only treatment planning.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>MVCTs and T1\u2010weighted MRIs for 120 patients treated for head\u2010and\u2010neck cancer were retrospectively acquired and co\u2010registered. A deep neural network based on a fully\u2010convolutional 3D U\u2010Net architecture was implemented to map MRI intensity to MVCT HU. Input to the model were volumetric patches generated from paired MRI and MVCT datasets. The U\u2010Net was initialized with random parameters and trained on a mean absolute error (MAE) objective function. Model accuracy was evaluated on 18 withheld test exams. sMVCTs were compared to respective MVCTs. Intensity\u2010modulated volumetric radiotherapy (IMRT) plans were generated on MVCTs of four different disease sites and compared to plans calculated onto corresponding sMVCTs using the gamma metric and dose\u2010volume\u2010histograms (DVHs).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>MAE values between sMVCT and MVCT datasets were 93.3\u00a0\u00b1\u00a027.5, 78.2\u00a0\u00b1\u00a027.5, and 138.0\u00a0\u00b1\u00a043.4 HU for whole body, soft tissue, and bone volumes, respectively. Overall, there was good agreement between sMVCT and MVCT, with bone and air posing the greatest challenges. The retrospective dataset introduced additional deviations due to sinus filling or tumor growth\/shrinkage between scans, differences in external contours due to variability in patient positioning, or when immobilization devices were absent from diagnostic MRIs. Dose distributions of IMRT plans evaluated for four test cases showed close agreement between sMVCT and MVCT images when evaluated using DVHs and gamma dose metrics, which averaged to 98.9\u00a0\u00b1\u00a01.0% and 96.8\u00a0\u00b1\u00a02.6% analyzed at 3%\/3\u00a0mm and 2%\/2\u00a0mm, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>MVCT datasets can be generated from T1\u2010weighted MRI using a 3D deep convolutional neural network with dose calculation on a sample sMVCT in close agreement with the MVCT. These results demonstrate the feasibility of using MRI\u2010derived sMVCT in an MR\u2010only treatment planning workflow.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15876","type":"journal-article","created":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T18:23:46Z","timestamp":1658600626000},"page":"6622-6634","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Generation of synthetic megavoltage CT for MRI\u2010only radiotherapy treatment planning using a 3D deep convolutional neural network"],"prefix":"10.1002","volume":"49","author":[{"given":"Jessica E.","family":"Scholey","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology The University of California, San Francisco  San Francisco California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhejit","family":"Rajagopal","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging The 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