{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T22:48:33Z","timestamp":1783637313560,"version":"3.55.0"},"reference-count":16,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T00:00:00Z","timestamp":1755216000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["NIH R01 EB028146"],"award-info":[{"award-number":["NIH R01 EB028146"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 AR066517"],"award-info":[{"award-number":["R01 AR066517"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 HL156818"],"award-info":[{"award-number":["R01 HL156818"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["NMR in Biomedicine"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>To assess lower back pain using quantitative chemical exchange saturation transfer (qCEST) imaging in a porcine model by comparing exchange rate maps obtained from multitasking qCEST with conventional qCEST. Use a permuted random forest (PRF) model trained on CEST\u2010derived magnetization transfer ratio (MTR) and exchange rate (<jats:italic>k<\/jats:italic><jats:sub><jats:italic>sw<\/jats:italic><\/jats:sub>) features to predict Glasgow pain scores. Six Yucatan minipigs were scanned at baseline and at four post\u2010injury time points (weeks 4, 8, 12, and 16) following intervertebral disc injury. Conventional qCEST imaging was performed at four B1 powers using a two\u2010dimensional reduced field of view turbo spin\u2010echo (TSE) sequence, with a total acquisition time of 24\u2009min per slice. Multitasking steady\u2010state (SS) CEST imaging was performed with pulsed saturation to achieve a steady state, acquiring 32 slices at 59 offsets for 4 B1 powers in 36\u2009min. Exchange rate maps were generated using omega plot analysis, and CEST images were analyzed using a multi\u2010pool fitting model to produce MTR and <jats:italic>k<\/jats:italic><jats:sub><jats:italic>sw<\/jats:italic><\/jats:sub> maps. Permuted random forest (PRF) model was trained on MTR and <jats:italic>k<\/jats:italic><jats:sub><jats:italic>sw<\/jats:italic><\/jats:sub> values to predict pain scores. Modic changes were assessed using T2\u2010weighted MR images. The Pearson correlation coefficient between exchange rate maps from multitasking qCEST and conventional qCEST was 0.82, demonstrating strong agreement. The 3D qCEST (SS\u2010CEST) technique effectively differentiated between healthy and injured discs, with injured discs exhibiting significantly higher <jats:italic>k<\/jats:italic><jats:sub><jats:italic>sw<\/jats:italic><\/jats:sub> values. Using MTR and <jats:italic>k<\/jats:italic><jats:sub><jats:italic>sw<\/jats:italic><\/jats:sub>, the PRF model achieved 80% accuracy in predicting pain scores disc\u2010by\u2010disc, outperforming the correlation with Modic changes (<jats:italic>r<\/jats:italic>\u2009=\u20090.45, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05); with a Cohen's Kappa of 0.4. 3D steady\u2010state qCEST with whole\u2010spine coverage can be done at 3T within 32\u2009min using MR Multitasking (acceleration factor of 22), and qCEST\u2010derived biomarkers (MTR and <jats:italic>k<\/jats:italic><jats:sub><jats:italic>sw<\/jats:italic><\/jats:sub>) can predict pain scores with an accuracy of 80%.<\/jats:p>","DOI":"10.1002\/nbm.70122","type":"journal-article","created":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T03:49:26Z","timestamp":1755316166000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Accelerated 3D qCEST of the Spine in a Porcine Model Using MR Multitasking at 3T"],"prefix":"10.1002","volume":"38","author":[{"given":"Karandeep","family":"Cheema","sequence":"first","affiliation":[{"name":"Biomedical Imaging Research Institute Cedars\u2010Sinai Medical Center  Los Angeles California USA"},{"name":"Department of Bioengineering University of California  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dante\u00a0Rigo","family":"De\u00a0Righi","sequence":"additional","affiliation":[{"name":"Biomedical Imaging Research Institute Cedars\u2010Sinai Medical Center  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chushu","family":"Shen","sequence":"additional","affiliation":[{"name":"Biomedical Imaging Research Institute Cedars\u2010Sinai Medical Center  Los Angeles California USA"},{"name":"Department of Bioengineering University of California  Los Angeles California 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