{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T01:34:43Z","timestamp":1790300083396,"version":"4.1.0"},"reference-count":41,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T00:00:00Z","timestamp":1687737600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>MRI\u2010only radiotherapy planning (MROP) is beneficial to patients by avoiding MRI\/CT registration errors, simplifying the radiation treatment simulation workflow and reducing exposure to ionizing radiation. MRI is the primary imaging modality for soft tissue delineation. Treatment planning CTs (i.e., CT simulation scan) are redundant if a synthetic CT (sCT) can be generated from the MRI to provide the patient positioning and electron density information. Unsupervised deep learning (DL) models like CycleGAN are widely used in MR\u2010to\u2010sCT conversion, when paired patient CT and MR image datasets are not available for model training. However, compared to supervised DL models, they cannot guarantee anatomic consistency, especially around bone.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The purpose of this work was to improve the sCT accuracy generated from MRI around bone for MROP.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To generate more reliable bony structures on sCT images, we proposed to add bony structure constraints in the unsupervised CycleGAN model's loss function and leverage Dixon constructed fat and in\u2010phase (IP) MR images. Dixon images provide better bone contrast than T2\u2010weighted images as inputs to a modified multi\u2010channel CycleGAN. A private dataset with a total of 31 prostate cancer patients were used for training (20) and testing (11).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We compared model performance with and without bony structure constraints using single\u2010 and multi\u2010channel inputs. Among all the models, multi\u2010channel CycleGAN with bony structure constraints had the lowest mean absolute error, both inside the bone and whole body (50.7 and 145.2 HU). This approach also resulted in the highest Dice similarity coefficient (0.88) of all bony structures compared with the planning CT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Modified multi\u2010channel CycleGAN with bony structure constraints, taking Dixon\u2010constructed fat and IP images as inputs, can generate clinically suitable sCT images in both bone and soft tissue. The generated sCT images have the potential to be used for accurate dose calculation and patient positioning in MROP radiation therapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.16556","type":"journal-article","created":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T08:12:29Z","timestamp":1687767149000},"page":"7368-7382","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Bony structure enhanced synthetic CT generation using Dixon sequences for pelvis MR\u2010only radiotherapy"],"prefix":"10.1002","volume":"50","author":[{"given":"Xiao","family":"Liang","sequence":"first","affiliation":[{"name":"Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology University of Texas Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Allen","family":"Yen","sequence":"additional","affiliation":[{"name":"Medical 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