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Med. Biol."],"published-print":{"date-parts":[[2026,7,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Introduction.<\/jats:italic>\n                    This study aims to generate synthetic-magnetic resonance imaging (\n                    <jats:italic>syn<\/jats:italic>\n                    MRI) for abdominal patients undergoing radiotherapy using deep inspiration breath hold (DIBH) on a conventional Linac.\n                    <jats:italic>Methods.<\/jats:italic>\n                    Extended cardiac-torso (XCAT) phantom and patient data were used to develop and test a patient-specific model to generate\n                    <jats:italic>syn<\/jats:italic>\n                    MRI. During patient simulation, 6 DIBH-MRIs and 1 DIBH-CT were acquired. One DIBH-MRI was set as the reference MRI (\n                    <jats:italic>ref<\/jats:italic>\n                    MRI) and principal component analysis (PCA) was performed using five deformation field maps (DFMs) by applying deformable image registration between\n                    <jats:italic>ref<\/jats:italic>\n                    MRI and the other DIBH-MRIs. PCA eigenvalues were sampled 1000 times to generate new deformations applied to a synthetic-CT with the same anatomic conditions as\n                    <jats:italic>ref<\/jats:italic>\n                    MRI. A convolutional neural network was trained to predict the eigenvalues corresponding to on-board conditions from a cone-beam CT (CBCT) to generate a final\n                    <jats:italic>syn<\/jats:italic>\n                    MRI with the new DFM. Four XCAT scenarios simulated changes from simulation to treatment. The model was evaluated using mean-absolute-error (MAE) and root-mean-square-error (RMSE), and the image quality was evaluated by structure-similarity-index metric (SSIM) and normalized RMSE (nRMSE). The accuracy of the predicted target volume for XCAT and fiducial clips for each patient was analyzed using center-of-mass-shift (COMS) between on-board conditions (ground-truth\n                    <jats:italic>syn<\/jats:italic>\n                    MRI for XCAT and CBCT for patients) and the predicted\n                    <jats:italic>syn<\/jats:italic>\n                    MRI. The liver dome difference was also evaluated.\n                    <jats:italic>Results.<\/jats:italic>\n                    Model performance yielded an MAE of 0.11 \u00b1 0.02 for XCAT, and 0.15 \u00b1 0.01, 0.15 \u00b1 0.02, 0.10 \u00b1 0.03, 0.12 \u00b1 0.09 for four patients, respectively. RMSE values were 0.11 \u00b1 0.07, 0.16 \u00b1 0.03, 0.19 \u00b1 0.01, 0.13 \u00b1 0.07, and 0.14 \u00b1 0.04. For image quality, SSIM values were 0.999 \u00b1 0.001 for XCAT, and 0.998 \u00b1 0.001, 0.995 \u00b1 0.002, 0.996 \u00b1 0.001, 0.998 \u00b1 0.001 for the patients. The nRMSE were 0.10 \u00b1 0.06, 0.58 \u00b1 0.03, 0.87 \u00b1 0.06, 0.39 \u00b1 0.04, and 0.35 \u00b1 0.02, respectively. For XCAT, the liver dome difference and tumor COMS were &lt;0.5 mm in all scenarios. For all patients, liver dome differences were &lt;1 mm, and fiducial COMSs were &lt;1.2 mm.\n                    <jats:italic>Conclusions<\/jats:italic>\n                    . The novel method generates\n                    <jats:italic>syn<\/jats:italic>\n                    MRI using on-board conditions in DIBH abdominal treatments performed on a conventional LINAC.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae7950","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T22:56:10Z","timestamp":1780700170000},"page":"135019","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["CBCT-based synthetic MRI generation for target localization during deep inspiration breath hold (DIBH) abdominal radiotherapy"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6574-1828","authenticated-orcid":true,"given":"Paulo","family":"Quintero","sequence":"first","affiliation":[{"name":"Memorial Sloan Kettering Cancer Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victoria","family":"Yu","sequence":"additional","affiliation":[{"name":"Memorial Sloan Kettering Cancer Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1171-4546","authenticated-orcid":true,"given":"Can","family":"Wu","sequence":"additional","affiliation":[{"name":"Memorial Sloan Kettering Cancer Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5585-966X","authenticated-orcid":true,"given":"Lauren","family":"Smith","sequence":"additional","affiliation":[{"name":"Memorial Sloan Kettering Cancer Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3782-4930","authenticated-orcid":true,"given":"Ricardo","family":"Otazo","sequence":"additional","affiliation":[{"name":"Memorial Sloan Kettering Cancer Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Cervi\u00f1o","sequence":"additional","affiliation":[{"name":"Memorial Sloan Kettering Cancer Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9423-0779","authenticated-orcid":true,"given":"Wendy","family":"Harris","sequence":"additional","affiliation":[{"name":"Memorial Sloan Kettering Cancer Center"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,7,3]]},"reference":[{"key":"pmbae7950bib1","first-page":"223","type":"journal-article","article-title":"Motion management in gastrointestinal cancers","volume":"5","author":"Abbas","year":"2014","journal-title":"J. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-01-22","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-06-05","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-07-03","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}