{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T01:52:45Z","timestamp":1790301165680,"version":"4.1.0"},"reference-count":41,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T00:00:00Z","timestamp":1754352000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To propose and evaluate an accelerated  quantification method that combines \u2010weighted fast spin echo (FSE) images and proton density (PD)\u2010weighted anatomical FSE images, leveraging deep learning models for  mapping. The goal is to reduce scan time and facilitate integration into routine clinical workflows for osteoarthritis (OA) assessment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This retrospective study utilized MRI data from 40 participants (30 OA patients and 10 healthy volunteers). A volume of PD\u2010weighted anatomical FSE images and a volume of \u2010weighted images acquired at a non\u2010zero spin\u2010lock time were used as input to train deep learning models, including a 2D U\u2010Net and a multi\u2010layer perceptron (MLP).  maps generated by these models were compared with ground truth maps derived from a traditional non\u2010linear least squares (NLLS) fitting method using four \u2010weighted images. Evaluation metrics included mean absolute error (MAE), mean absolute percentage error (MAPE), regional error (RE), and regional percentage error (RPE).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The best\u2010performed deep learning models achieved RPEs below 5% across all evaluated scenarios. This performance was consistent even in reduced acquisition settings that included only one PD\u2010weighted image and one \u2010weighted image, where NLLS methods cannot be applied. Furthermore, the results were comparable to those obtained with NLLS when longer acquisitions with four \u2010weighted images were used.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The proposed approach enables efficient  mapping using PD\u2010weighted anatomical images, reducing scan time while maintaining clinical standards. This method has the potential to facilitate the integration of quantitative MRI techniques into routine clinical practice, benefiting OA diagnosis and monitoring.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mrm.70022","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T10:01:49Z","timestamp":1754388109000},"page":"2745-2757","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Utilizing 3D fast spin echo anatomical imaging to reduce the number of contrast preparations in T1\u03c1$$ {T}_{1\\rho } $$ quantification of knee cartilage using learning\u2010based methods"],"prefix":"10.1002","volume":"94","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3897-9280","authenticated-orcid":false,"given":"Junru","family":"Zhong","sequence":"first","affiliation":[{"name":"CU Lab of AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology The Chinese University of Hong Kong  Hong Kong 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