{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T16:03:27Z","timestamp":1789229007143,"version":"build-2803163510"},"reference-count":58,"publisher":"IOP Publishing","issue":"11","license":[{"start":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T00:00:00Z","timestamp":1716940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T00:00:00Z","timestamp":1716940800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["No. 2022R1A2C2008623"],"award-info":[{"award-number":["No. 2022R1A2C2008623"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology","doi-asserted-by":"crossref","award":["Project No. P0019304"],"award-info":[{"award-number":["Project No. P0019304"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"crossref"}]},{"name":"The Alvin J. Siteman Cancer Center","award":["No. P30 CA091842"],"award-info":[{"award-number":["No. P30 CA091842"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2024,6,7]]},"abstract":"<jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . Deep-learning networks for super-resolution (SR) reconstruction enhance the spatial-resolution of 3D magnetic resonance imaging (MRI) for MR-guided radiotherapy (MRgRT). However, variations between MRI scanners and patients impact the quality of SR for real-time 3D low-resolution (LR) cine MRI. In this study, we present a personalized super-resolution (psSR) network that incorporates transfer-learning to overcome the challenges in inter-scanner SR of 3D cine MRI.\n                    <jats:italic>Approach<\/jats:italic>\n                    : Development of the proposed psSR network comprises two-stages: (1) a cohort-specific SR (csSR) network using clinical patient datasets, and (2) a psSR network using transfer-learning to target datasets. The csSR network was developed by training on breath-hold and respiratory-gated high-resolution (HR) 3D MRIs and their k-space down-sampled LR MRIs from 53 thoracoabdominal patients scanned at 1.5 T. The psSR network was developed through transfer-learning to retrain the csSR network using a single breath-hold HR MRI and a corresponding 3D cine MRI from 5 healthy volunteers scanned at 0.55 T. Image quality was evaluated using the peak-signal-noise-ratio (PSNR) and the structure-similarity-index-measure (SSIM). The clinical feasibility was assessed by liver contouring on the psSR MRI using an auto-segmentation network and quantified using the dice-similarity-coefficient (DSC).\n                    <jats:italic>Results<\/jats:italic>\n                    . Mean PSNR and SSIM values of psSR MRIs were increased by 57.2% (13.8\u201321.7) and 94.7% (0.38\u20130.74) compared to cine MRIs, with the reference 0.55 T breath-hold HR MRI. In the contour evaluation, DSC was increased by 15% (0.79\u20130.91). Average time consumed for transfer-learning was 90 s, psSR was 4.51 ms per volume, and auto-segmentation was 210 ms, respectively.\n                    <jats:italic>Significance<\/jats:italic>\n                    . The proposed psSR reconstruction substantially increased image and segmentation quality of cine MRI in an average of 215 ms across the scanners and patients with less than 2 min of prerequisite transfer-learning. This approach would be effective in overcoming cohort- and scanner-dependency of deep-learning for MRgRT.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ad43ab","type":"journal-article","created":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T18:42:17Z","timestamp":1714070537000},"page":"115038","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Inter-scanner super-resolution of 3D cine MRI using a transfer-learning network for MRgRT"],"prefix":"10.1088","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9486-738X","authenticated-orcid":true,"given":"Young Hun","family":"Yoon","sequence":"first","affiliation":[{"name":"Yonsei University College of Medicine"},{"name":"Yonsei University College of Medicine"},{"name":"Washington University in St. Louis"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaehee","family":"Chun","sequence":"additional","affiliation":[{"name":"Oncosoft Inc., Seoul,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kendall","family":"Kiser","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0713-4418","authenticated-orcid":true,"given":"Shanti","family":"Marasini","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Austen","family":"Curcuru","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H Michael","family":"Gach","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis"},{"name":"Washington University in St. Louis"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin Sung","family":"Kim","sequence":"additional","affiliation":[{"name":"Yonsei University College of Medicine"},{"name":"Yonsei University College of Medicine"},{"name":"Oncosoft Inc., Seoul,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9809-9587","authenticated-orcid":true,"given":"Taeho","family":"Kim","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2024,5,29]]},"reference":[{"key":"pmbad43abbib1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab2ba8","type":"journal-article","article-title":"Real-time intrafraction motion monitoring in external beam radiotherapy","volume":"64","author":"Bertholet","year":"2019","journal-title":"Phys. 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