{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T22:24:18Z","timestamp":1793139858234,"version":"4.5.0"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T00:00:00Z","timestamp":1644278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100013027","name":"Calvary Mater Newcastle","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013027","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>There are several means of synthetic computed tomography (sCT) generation for magnetic resonance imaging (MRI)-only planning; however, much of the research omits large pelvic treatment regions and female anatomical specific methods. This research aimed to apply four of the most popular methods of sCT creation to facilitate MRI-only radiotherapy treatment planning for male and female anorectal and gynecological neoplasms. sCT methods were validated against conventional computed tomography (CT), with regard to Hounsfield unit (HU) estimation and plan dosimetry.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and Materials<\/jats:title><jats:p>Paired MRI and CT scans of 40 patients were used for sCT generation and validation. Bulk density assignment, tissue class density assignment, hybrid atlas, and deep learning sCT generation methods were applied to all 40 patients. Dosimetric accuracy was assessed by dose difference at reference point, dose volume histogram (DVH) parameters, and 3D gamma dose comparison. HU estimation was assessed by mean error and mean absolute error in HU value between each sCT and CT.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The median percentage dose difference between the CT and sCT was &amp;lt;1.0% for all sCT methods. The deep learning method resulted in the lowest median percentage dose difference to CT at \u22120.03% (IQR 0.13, \u22120.31) and bulk density assignment resulted in the greatest difference at \u22120.73% (IQR \u22120.10, \u22121.01). The mean 3D gamma dose agreement at 3%\/2 mm among all sCT methods was 99.8%. The highest agreement at 1%\/1 mm was 97.3% for the deep learning method and the lowest was 93.6% for the bulk density method. Deep learning and hybrid atlas techniques gave the lowest difference to CT in mean error and mean absolute error in HU estimation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>All methods of sCT generation used in this study resulted in similarly high dosimetric agreement for MRI-only planning of male and female cancer pelvic regions. The choice of the sCT generation technique can be guided by department resources available and image guidance considerations, with minimal impact on dosimetric accuracy.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fonc.2022.822687","type":"journal-article","created":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T06:58:00Z","timestamp":1644303480000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Comparison of Synthetic Computed Tomography Generation Methods, Incorporating Male and Female Anatomical Differences, for Magnetic Resonance Imaging-Only Definitive Pelvic Radiotherapy"],"prefix":"10.3389","volume":"12","author":[{"given":"Laura M.","family":"O\u2019Connor","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae H.","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason A.","family":"Dowling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helen","family":"Warren-Forward","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jarad","family":"Martin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter B.","family":"Greer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,2,8]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-14442-5_8","article-title":"Challenges and Requirements","volume-title":"MRI for Radiotherapy. 1st Ed","author":"Tyagi","year":"2019"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1259\/bjr\/11855927","article-title":"An Evaluation of Four CT\u2013MRI Co-Registration Techniques for Radiotherapy Treatment Planning of Prone Rectal Cancer Patients","volume":"85","author":"Dean","year":"2012","journal-title":"Br J Radiol"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/s1538-4721(03)00133-8","article-title":"Image Fusion of CT and MRI Data Enables Improved Target Volume Definition in 3D-Brachytherapy Treatment Planning","volume":"2","author":"Krempien","year":"2003","journal-title":"Brachytherapy"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aada6d","article-title":"Dose Evaluation of Fast Synthetic-CT Generation Using a Generative Adversarial Network for General Pelvis MR-Only Radiotherapy","volume":"63","author":"Maspero","year":"2018","journal-title":"Phys Med Biol"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.phro.2019.06.001","article-title":"Assessment of Dosimetric and Positioning Accuracy of a Magnetic Resonance Imaging-Only Solution for External Beam Radiotherapy of Pelvic Anatomy","volume":"11","author":"Kemppainen","year":"2019","journal-title":"Phys Imaging Radiat Oncol"},{"key":"B6","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.phro.2018.09.002","article-title":"Feasibility of Magnetic Resonance Imaging-Only Rectum Radiotherapy With a Commercial Synthetic Computed Tomography Generation Solution","volume":"7","author":"Maspero","year":"2018","journal-title":"Phys Imaging Radiat Oncol"},{"key":"B7","doi-asserted-by":"publisher","first-page":"e0190883","DOI":"10.1371\/journal.pone.0190883","article-title":"Dosimetric Evaluation of Magnetic Resonance-Generated Synthetic CT for Radiation Treatment of Rectal Cancer","volume":"13","author":"Wang","year":"2018","journal-title":"PLoS One"},{"key":"B8","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.ijrobp.2017.08.043","article-title":"Systematic Review of Synthetic Computed Tomography Generation Methodologies for Use in Magnetic Resonance Imaging\u2013Only Radiation Therapy","volume":"100","author":"Johnstone","year":"2018","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B9","volume-title":"White Paper: MR-Only RT Planning for Brain and Pelvis With Synthetic Ct","year":"2019"},{"key":"B10","volume-title":"MR-Only Simulation for Radiotherapy Planning. 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