{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T16:42:07Z","timestamp":1792514527684,"version":"4.5.0"},"reference-count":50,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:00:00Z","timestamp":1792454400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:00:00Z","timestamp":1792454400000},"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":[[2026,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Computed Tomography (CT) provides geometric accuracy and electron density information essential for radiotherapy planning; however, it offers limited soft\u2010tissue contrast. Magnetic Resonance Imaging (MRI) improves tumor visualization but introduces registration uncertainties when acquired separately from the radiotherapy planning CT (pCT). Cross\u2010modality image synthesis enables the generation of synthetic MRI (sMRI) directly from pCT and may combine the strengths of both modalities, reducing reliance on MRI access.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>We propose a two\u2010stage deep\u2010learning framework for generating sMRI from pCT image\u2010sets specifically in head\u2010and\u2010neck (H&amp;N) cancers, providing soft\u2010tissue contrast comparable to ground\u2010truth MR images (gtMRI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study included 60 patient cases with H&amp;N cancer treated with radical radiotherapy between March 2022 and April 2024 for model development (50 for training, 10 for validation), plus 10 additional cases, independent of the development cohort for testing. All patients underwent a standard, non\u2010contrast pCT scan followed by an MRI acquired on a 3T Siemens MR simulator. Images were deformably\u2010registered, yielding 10,000 matched CT\u2013MRI slice\u2010pairs for model training. The proposed model utilizes a two\u2010stage architecture, a Pix2Pix conditional generative adversarial network (GAN) for coarse sMRI synthesis, followed by a conditional diffusion model for refinement. To test the model's robustness, 10 patient cases with both pCT and contrast\u2010enhanced CT (ceCT) were used to generate sMRI. Model performance was assessed using structural similarity index (SSIM), multi\u2010scale structural similarity index (MS\u2010SSIM), mean absolute error (MAE), and peak signal\u2010to\u2010noise ratio (PSNR). In addition, the geometric agreement of the primary (GTVp_70) and nodal (GTVn_70) gross tumor volumes delineated on sMRI vs. gtMRI was independently assessed by two radiation oncologists (ROs) using the Dice similarity coefficient (DSC), 95\n                      <jats:sup>th<\/jats:sup>\n                      \u2010percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The average MS\u2010SSIM, SSIM, MAE and PSNR for pCT\u2010derived sMRI were 0.82\u00a0\u00b1\u00a00.02, 0.70\u00a0\u00b1\u00a00.03, 0.05\u00a0\u00b1\u00a00.01, and 20.42\u00a0\u00b1\u00a00.66\u00a0dB, respectively. The mean MS\u2010SSIM and SSIM values for ceCT\u2010derived sMRI were 0.02 lower than those for the corresponding pCT\u2010to\u2010sMRI metrics. For the contour\u2010based evaluation, sMRI\u2010derived GTV contours achieved a mean DSC of 0.56 and 0.60 for GTVp_70, and 0.72 and 0.74 for GTVn_70 across the two ROs, relative to gtMRI contours.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our proof\u2010of\u2010concept study demonstrates the feasibility of pCT\u2010derived sMRI for H&amp;N cancers using a deep\u2010learning framework. The model produces grayscale patterns and soft\u2010tissue contrast that closely resemble the gtMRI.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70849","type":"journal-article","created":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T15:51:27Z","timestamp":1792511487000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Synthetic MRI from radiotherapy planning CT images for Head\u2010and\u2010Neck Cancer"],"prefix":"10.1002","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5023-1558","authenticated-orcid":false,"given":"Prabhakar","family":"Ramachandran","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Princess Alexandra Hospital  Woolloongabba Queensland Australia"},{"name":"Australian Institute for Bioengineering and Nanotechnology The University of Queensland  St Lucia Queensland Australia"},{"name":"School of Chemistry and Physics Queensland University of Technology  Brisbane Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akshay C","family":"Dhargave","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Princess Alexandra Hospital  Woolloongabba Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine","family":"Jones","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Princess Alexandra Hospital  Woolloongabba Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dionee","family":"Liefman","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Princess Alexandra Hospital  Woolloongabba Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lachlan","family":"McDowell","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Princess Alexandra Hospital  Woolloongabba Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Howard","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Princess Alexandra Hospital  Woolloongabba Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,20]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.semnuclmed.2020.07.005"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/0284186x.2017.1287427"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13244\u2010024\u201001799\u20101"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/curroncol29110655"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.614008"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.a7773"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102531"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2021.03.021"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.5114\/jcb.2020.100386"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12155"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2018.09.002"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.21037\/qims\u201020\u20101056"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15150"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010023\u201002209\u20104"},{"key":"e_1_2_12_16_1","first-page":"234","volume-title":"Medical Image Computing and Computer\u2010Assisted Intervention\u2014MICCAI","author":"Ronneberger O","year":"2015"},{"key":"e_1_2_12_17_1","first-page":"2672","volume-title":"Advances in Neural Information Processing Systems","author":"Goodfellow I","year":"2014"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10334\u2010021\u201000974\u20105"},{"key":"e_1_2_12_19_1","doi-asserted-by":"publisher","DOI":"10.4103\/jmp.jmp_140_24"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978\u20103\u2010030\u201000928\u20101_60"},{"key":"e_1_2_12_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010023\u201034341\u20102"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916\u2010025\u201002300\u20101"},{"key":"e_1_2_12_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102846"},{"key":"e_1_2_12_24_1","doi-asserted-by":"publisher","DOI":"10.26599\/tst.2024.9010047"},{"key":"e_1_2_12_25_1","first-page":"277","volume-title":"Intraoperative Imaging and Image\u2010Guided Therapy","author":"Kikinis R","year":"2014"},{"key":"e_1_2_12_26_1","doi-asserted-by":"crossref","unstructured":"IsolaP ZhuJY ZhouT EfrosAAImage\u2010to\u2010image translation with conditional adversarial networks. 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