{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T12:47:36Z","timestamp":1793710056721,"version":"4.6.1"},"reference-count":47,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>The Osteoarthritis Initiative (OAI) collected extensive imaging data, including Multi\u2010Echo Spin\u2010Echo (MESE) sequences for measuring knee cartilage <jats:italic>T<\/jats:italic><jats:sub>2<\/jats:sub> relaxation times. Mono\u2010exponential models are used in the OAI for <jats:italic>T<\/jats:italic><jats:sub>2<\/jats:sub> fitting, which neglects stimulated echoes and <jats:italic>B<\/jats:italic><jats:sub>1<\/jats:sub> inhomogeneities. Extended Phase Graph (EPG) modeling addresses these limitations but has not been applied to the OAI dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To assess how different fitting methods, including EPG\u2010based and exponential\u2010based approaches, affect the accuracy and reproducibility of cartilage <jats:italic>T<\/jats:italic><jats:sub>2<\/jats:sub> in the OAI dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Retrospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>From OAI dataset, 50 subjects, stratified by osteoarthritis (OA) severity using Kellgren\u2013Lawrence grades (KLG), and 50 subjects without OA diagnosis during OAI duration were selected (each group: 25 females, mean ages ~61\u2009years).<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>3\u2010T, two\u2010dimensional (2D) MESE sequence.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>Femoral and tibial cartilages were segmented from DESS images, subdivided into seven sub\u2010regions, and co\u2010registered to MESE. <jats:italic>T<\/jats:italic><jats:sub>2<\/jats:sub> maps were obtained using three EPG\u2010based methods (nonlinear least squares, dictionary matching, and deep learning) and three mono\u2010exponential approaches (linear least squares, nonlinear least squares, and noise\u2010corrected exponential). Average <jats:italic>T<\/jats:italic><jats:sub>2<\/jats:sub> values within sub\u2010regions were obtained. Pair\u2010wise agreement among fitting methods was evaluated using the stratified subjects, while reproducibility using healthy subjects. Each method's <jats:italic>T<\/jats:italic><jats:sub>2<\/jats:sub> accuracy and repeatability varying signal\u2010to\u2010noise ratio (SNR) were assessed with simulations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Bland\u2013Altman analysis, Lin's concordance coefficient, and coefficient of variation assessed agreement, repeatability, and reproducibility. Statistical significance was set at <jats:italic>P<\/jats:italic>\u2010value &lt;0.05.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>EPG\u2010based methods demonstrated superior <jats:italic>T<\/jats:italic><jats:sub>2<\/jats:sub> accuracy (mean absolute error below 0.5\u2009msec at SNR\u2009&gt;\u2009100) compared to mono\u2010exponential methods (error\u2009&gt;\u20097\u2009msec). EPG\u2010based approaches had better reproducibility, with limits of agreement 1.5\u20135\u2009msec narrower than exponential\u2010based methods. <jats:italic>T<\/jats:italic><jats:sub>2<\/jats:sub> values from EPG methods were systematically 10\u201317\u2009msec lower than those from mono\u2010exponential fitting.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>EPG modeling improved agreement and reproducibility of cartilage <jats:italic>T<\/jats:italic><jats:sub>2<\/jats:sub> mapping in subjects from the OAI dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Evidence Level<\/jats:title><jats:p>3<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 1<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.29646","type":"journal-article","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T18:30:16Z","timestamp":1730140216000},"page":"2116-2127","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Improving Accuracy and Reproducibility of Cartilage <scp><i>T<\/i><sub>2<\/sub><\/scp> Mapping in the <scp>OAI<\/scp> Dataset Through Extended Phase Graph Modeling"],"prefix":"10.1002","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8795-1073","authenticated-orcid":false,"given":"Marco","family":"Barbieri","sequence":"first","affiliation":[{"name":"Department of Radiology Stanford University  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony A.","family":"Gatti","sequence":"additional","affiliation":[{"name":"Department of Radiology Stanford University  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feliks","family":"Kogan","sequence":"additional","affiliation":[{"name":"Department of Radiology Stanford University  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_2_7_2_1","unstructured":"Branch NSC and O. 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