{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T20:30:40Z","timestamp":1787689840490,"version":"build-2784847793"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Yonsei University College","award":["6-2022-0024"],"award-info":[{"award-number":["6-2022-0024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>Temporomandibular disorder (TMD) patients experience a variety of clinical symptoms, and MRI is the most effective tool for diagnosing temporomandibular joint (TMJ) disc displacement. This study aimed to develop a transformer-based deep learning model to generate T2-weighted (T2w) images from proton density-weighted (PDw) images, reducing MRI scan time for TMD patients.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>A dataset of 7226 images from 178 patients who underwent TMJ MRI examinations was used. The proposed model employed a generative adversarial network framework with a TransUNet architecture as the generator for image translation. Additionally, a disc segmentation decoder was integrated to improve image quality in the TMJ disc region. The model performance was evaluated using metrics such as the structural similarity index measure (SSIM), learned perceptual image patch similarity (LPIPS), and Fr\u00e9chet inception distance (FID). Three experienced oral radiologists also performed a qualitative assessment through the mean opinion score (MOS).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The model demonstrated high performance in generating T2w images from PDw images, achieving average SSIM, LPIPS, and FID values of 82.28%, 2.46, and 23.85, respectively, in the disc region. The model also obtained an average MOS score of 4.58, surpassing other models. Additionally, the model showed robust segmentation capabilities for the TMJ disc.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>The proposed model, integrating a transformer and a disc segmentation task, demonstrated strong performance in MR image generation, both quantitatively and qualitatively. This suggests its potential clinical significance in reducing MRI scan times for TMD patients while maintaining high image quality.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/dmfr\/twaf017","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T08:54:11Z","timestamp":1742374451000},"page":"357-363","source":"Crossref","is-referenced-by-count":5,"title":["Magnetic resonance image generation using enhanced TransUNet in temporomandibular disorder patients"],"prefix":"10.1093","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4436-1751","authenticated-orcid":false,"given":"Eun-Gyu","family":"Ha","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Yonsei University College of Engineering , Seoul 03722,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5862-2975","authenticated-orcid":false,"given":"Kug Jin","family":"Jeon","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry , Seoul 03722,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8943-4192","authenticated-orcid":false,"given":"Chena","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry , Seoul 03722,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6717-7770","authenticated-orcid":false,"given":"Dong-Hyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Yonsei University College of Engineering , Seoul 03722,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1775-7862","authenticated-orcid":false,"given":"Sang-Sun","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry , Seoul 03722,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"key":"2025063003530917300_twaf017-B1","volume-title":"Reference Module in Biomedical Sciences","author":"Pandarakalam","year":"2014"},{"key":"2025063003530917300_twaf017-B2","doi-asserted-by":"crossref","first-page":"283163","DOI":"10.1155\/2012\/283163","article-title":"Analysis of criteria for MRI diagnosis of TMJ disc displacement and arthralgia","volume":"2012","author":"Shaefer","year":"2012","journal-title":"Int J Dent."},{"key":"2025063003530917300_twaf017-B3","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/B978-0-12-813886-1.00008-5","volume-title":"Bioengineering Innovative Solutions for Cancer","author":"Bangerter","year":"2020"},{"key":"2025063003530917300_twaf017-B4","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1016\/B978-0-12-801238-3.99945-8","volume-title":"Encyclopedia of Biomedical Engineering","author":"Chan","year":"2019"},{"key":"2025063003530917300_twaf017-B5","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1002\/jmri.24068","article-title":"Spin echo magnetic resonance imaging","volume":"37","author":"Jung","year":"2013","journal-title":"J Magn Reson Imaging."},{"key":"2025063003530917300_twaf017-B6","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.21037\/qims-20-67","article-title":"Determining the optimal magnetic resonance imaging sequences for the efficient diagnosis of temporomandibular joint disorders","volume":"11","author":"Dong","year":"2021","journal-title":"Quant Imaging Med Surg."},{"key":"2025063003530917300_twaf017-B7","doi-asserted-by":"crossref","first-page":"103046","DOI":"10.1016\/j.media.2023.103046","article-title":"Deep learning based synthesis of MRI, CT and PET: review and analysis","volume":"92","author":"Dayarathna","year":"2024","journal-title":"Med Image Anal"},{"key":"2025063003530917300_twaf017-B8","doi-asserted-by":"crossref","first-page":"102802","DOI":"10.1016\/j.media.2023.102802","article-title":"Transformers in medical imaging: a survey","volume":"88","author":"Shamshad","year":"2023","journal-title":"Med Image Anal"},{"key":"2025063003530917300_twaf017-B9","doi-asserted-by":"crossref","first-page":"2577","DOI":"10.1109\/TMI.2023.3261707","article-title":"One model to synthesize them all: multi-contrast multi-scale transformer for missing data imputation","volume":"42","author":"Liu","year":"2023","journal-title":"IEEE Trans Med Imaging."},{"key":"2025063003530917300_twaf017-B10","first-page":"30","volume-title":"Advances in Neural Information Processing Systems. 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