{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T03:47:07Z","timestamp":1788839227168,"version":"build-2803163510"},"reference-count":80,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AF-ACL consortium","award":["NIH UH3AR076724"],"award-info":[{"award-number":["NIH UH3AR076724"]}]},{"name":"AF-ACL consortium","award":["NIH R01AR078762"],"award-info":[{"award-number":["NIH R01AR078762"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>A 2D U-Net was trained to generate synthetic T1p maps from T2 maps for knee MRI to explore the feasibility of domain adaptation for enriching existing datasets and enabling rapid, reliable image reconstruction. The network was developed using 509 healthy contralateral and injured ipsilateral knee images from patients with ACL injuries and reconstruction surgeries acquired across three institutions. Network generalizability was evaluated on 343 knees acquired in a clinical setting and 46 knees from simultaneous bilateral acquisition in a research setting. The deep neural network synthesized high-fidelity reconstructions of T1p maps, preserving textures and local T1p elevation patterns in cartilage with a normalized mean square error of 2.4% and Pearson\u2019s correlation coefficient of 0.93. Analysis of reconstructed T1p maps within cartilage compartments revealed minimal bias (\u22120.10 ms), tight limits of agreement, and quantification error (5.7%) below the threshold for clinically significant change (6.42%) associated with osteoarthritis. In an out-of-distribution external test set, synthetic maps preserved T1p textures, but exhibited increased bias and wider limits of agreement. This study demonstrates the capability of image synthesis to reduce acquisition time, derive meaningful information from existing datasets, and suggest a pathway for standardizing T1p as a quantitative biomarker for osteoarthritis.<\/jats:p>","DOI":"10.3390\/bioengineering11010017","type":"journal-article","created":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T20:42:30Z","timestamp":1703450550000},"page":"17","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Synthetic Knee MRI T1p Maps as an Avenue for Clinical Translation of Quantitative Osteoarthritis Biomarkers"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8766-7542","authenticated-orcid":false,"given":"Michelle W.","family":"Tong","sequence":"first","affiliation":[{"name":"Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA"},{"name":"Department of Bioengineering, University of California Berkeley, Berkeley, CA 94720, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3974-4838","authenticated-orcid":false,"given":"Aniket A.","family":"Tolpadi","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA"},{"name":"Department of Bioengineering, University of California Berkeley, Berkeley, CA 94720, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9893-3527","authenticated-orcid":false,"given":"Rupsa","family":"Bhattacharjee","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Misung","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharmila","family":"Majumdar","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valentina","family":"Pedoia","sequence":"additional","affiliation":[{"name":"Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"299","DOI":"10.21037\/atm.2020.02.175","article-title":"Global Low Back Pain Prevalence and Years Lived with Disability from 1990 to 2017: Estimates from the Global Burden of Disease Study 2017","volume":"8","author":"Wu","year":"2020","journal-title":"Ann. 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