{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T01:25:13Z","timestamp":1793064313137,"version":"4.5.0"},"reference-count":45,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>In external beam radiation therapy (EBRT) for prostate cancer, both MRI and CT are typically used\u2014CT provides electron density for dose calculation and visualization of bony anatomy and fiducial markers, while MRI offers superior soft tissue contrast. With recent advances in deep learning, synthetic CT (sCT) images can now be generated from MRI, potentially eliminating the need for separate CT scans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To clinically implement an MRI\u2010only planning (MROP) workflow for both X\u2010ray and MRI\u2010guided systems.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>MROP implementation involved optimizing MRI simulation protocols, developing a deep learning\u2010based sCT generation method, and automating fiducial marker detection. A multichannel CycleGAN model was trained using T1\u2010weighted Dixon images to generate sCT. For SBRT cases, the immobilization device was auto\u2010inserted into sCT images. Fiducial markers were detected using a quantitative susceptibility mapping (QSM) method. Validation included retrospective comparison of Hounsfield units and dosimetric plans to those based on planning CT for 11 patients, followed by prospective evaluation of ten patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The MRI protocol included T2\u2010weighted 3D imaging for contouring, T1\u2010weighted Dixon for sCT, and T2*\u2010weighted GRE for fiducial detection. The MROP workflow was prospectively applied in 10 patients (three SBRT, seven conventional). Dosimetric comparisons showed &lt;1% difference between MROP and CT\u2010based plans across all PTVs. The QSM\u2010based fiducial detection achieved a 95.5% success rate (63\/66 markers) and reduced detection time from \u223c10 to 2 min per patient. All MROP components were integrated into the treatment planning system via scripting. Implementation reduced simulation scheduling time by 7.1 days.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We successfully implemented an MROP workflow for prostate cancer EBRT, eliminating the need for CT, reducing radiation exposure, and improving clinical efficiency. Over 500 patients have been treated to date using this approach on both C\u2010arm and MR\u2010guided systems.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70228","type":"journal-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T01:35:13Z","timestamp":1755826513000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Development and clinical implementation of an MRI\u2010only planning workflow featuring deep learning\u2010based synthetic CT for prostate cancer external beam radiotherapy"],"prefix":"10.1002","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2518-7989","authenticated-orcid":false,"given":"Jie","family":"Deng","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology University of Texas Southwestern Medical Center  Texas 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