{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T12:19:06Z","timestamp":1768565946419,"version":"3.49.0"},"posted":{"date-parts":[[2024,4,29]]},"group-title":"Orthopedics","reference-count":55,"publisher":"openRxiv","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2024,4,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Key terms<\/jats:title>\n                  <jats:p>Multicontrast and Multiparametric, Magnetic Resonance Imaging, Osteoarthritis, Functional Biomechanical Imaging, Knee Joint Degeneration<\/jats:p>\n                  <jats:p>\n                    <jats:bold>What is known about the subject:<\/jats:bold>\n                    dualMRI has been used to quantify strains in a healthy human population\n                    <jats:italic>in vivo<\/jats:italic>\n                    and in cartilage explant models. Previously, OA severity, as determined by histology, has been positively correlated to increased shear and transverse strains in cartilage explants.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>What this study adds to existing knowledge:<\/jats:bold>\n                    This is the first\n                    <jats:italic>in vivo<\/jats:italic>\n                    use of dualMRI in a participant demographic post-ACL reconstruction and at risk for developing osteoarthritis. This study shows that dualMRI-derived strains are more significantly correlated with patient-reported outcomes than any MRI relaxometry metric.\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Anterior cruciate ligament (ACL) injuries lead to an increased risk of osteoarthritis, characterized by altered cartilage tissue structure and function. Displacements under applied loading by magnetic resonance imaging (dualMRI) is a novel MRI technique that can be used to quantify mechanical strain in cartilage while undergoing a physiological load.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>To determine if strains derived by dualMRI and relaxometry measures correlate with patient-reported outcomes at six months post unilateral ACL reconstruction.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Study Design<\/jats:title>\n                  <jats:p>Cohort study<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Quantitative MRI (T2, T2*, T1\u03c1) measurements and transverse, axial, and shear strains were quantified in the medial articular tibiofemoral cartilage of 35 participants at six-months post unilateral ACL reconstruction. The relationships between patient-reported outcomes (WOMAC, KOOS, MARS) and all qMRI relaxation times were quantified using general linear mixed-effects models. A combined best-fit multicontrast MRI model was then developed using backwards regression to determine the patient features and MRI metrics that are most predictive of patient-reported outcome scores.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Higher femoral strains were significantly correlated with worse patient-reported functional outcomes. Femoral shear and transverse strains were positively correlated with six-month KOOS and WOMAC scores, after controlling for covariates. No relaxometry measures were correlated with patient-reported outcome scores. We identified the best-fit model for predicting WOMAC score using multiple MRI measures and patient-specific information, including sex, age, graft type, femoral transverse strain, femoral axial strain, and femoral shear strain. The best-fit model significantly predicted WOMAC score (p&lt;0.001) better than any one individual MRI metric alone. When we regressed the model-predicted WOMAC scores against the patient-reported WOMAC scores, we found that our model achieved a goodness of fit exceeding 0.52.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>\n                    This work presents the first use of dualMRI\n                    <jats:italic>in vivo<\/jats:italic>\n                    in a cohort of participants at risk for developing osteoarthritis. Our results indicate that both shear and transverse strains are highly correlated with patient-reported outcome severity could serve as novel imaging biomarkers to predict the development of osteoarthritis.\n                  <\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2024.04.27.24306484","type":"posted-content","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T15:40:10Z","timestamp":1714405210000},"source":"Crossref","is-referenced-by-count":1,"title":["MRI-derived Articular Cartilage Strains Predict Patient-Reported Outcomes Six Months Post Anterior Cruciate Ligament Reconstruction"],"prefix":"10.64898","author":[{"given":"Emily Y.","family":"Miller","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Woowon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timothy","family":"Lowe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongtian","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pablo F.","family":"Argote","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danielle","family":"Dresdner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Kelly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rachel M.","family":"Frank","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"McCarty","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan","family":"Bravman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Stokes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nancy C.","family":"Emery","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8256-5683","authenticated-orcid":false,"given":"Corey P.","family":"Neu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"issue":"10","key":"2024050207351203000_2024.04.27.24306484v1.1","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1016\/j.joca.2015.05.028","article-title":"Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging","volume":"23","year":"2015","journal-title":"Osteoarthritis Cartilage"},{"issue":"25","key":"2024050207351203000_2024.04.27.24306484v1.2","doi-asserted-by":"crossref","first-page":"2190","DOI":"10.1097\/BRS.0b013e31820287bf","article-title":"T1\u03c1 magnetic resonance imaging and discography pressure as novel biomarkers for disc degeneration and low back pain","volume":"36","year":"2011","journal-title":"Spine (Phila Pa 1976)"},{"key":"2024050207351203000_2024.04.27.24306484v1.3","doi-asserted-by":"crossref","unstructured":"Buck AN , Lisee CM , Bjornsen ES , et al. 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