{"doi":"10.1002/nbm.70122","title":"Accelerated 3D qCEST of the Spine in a Porcine Model Using MR Multitasking at 3T","abstract":"ABSTRACT 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‐derived magnetization transfer ratio (MTR) and exchange rate ( k sw ) features to predict Glasgow pain scores. Six Yucatan minipigs were scanned at baseline and at four post‐injury time points (weeks 4, 8, 12, and 16) following intervertebral disc injury. Conventional qCEST imaging was performed at four B1 powers using a two‐dimensional reduced field of view turbo spin‐echo (TSE) sequence, with a total acquisition time of 24 min per slice. Multitasking steady‐state (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 min. Exchange rate maps were generated using omega plot analysis, and CEST images were analyzed using a multi‐pool fitting model to produce MTR and k sw maps. Permuted random forest (PRF) model was trained on MTR and k sw values to predict pain scores. Modic changes were assessed using T2‐weighted 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‐CEST) technique effectively differentiated between healthy and injured discs, with injured discs exhibiting significantly higher k sw values. Using MTR and k sw , the PRF model achieved 80% accuracy in predicting pain scores disc‐by‐disc, outperforming the correlation with Modic changes ( r = 0.45, p < 0.05); with a Cohen's Kappa of 0.4. 3D steady‐state qCEST with whole‐spine coverage can be done at 3T within 32 min using MR Multitasking (acceleration factor of 22), and qCEST‐derived biomarkers (MTR and k sw ) can predict pain scores with an accuracy of 80%.","journal":"NMR in Biomedicine","year":2025,"id":524176,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9386,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1398250,"name":"Dante Rigo De Righi","orcid":null,"position":1,"is_corresponding":false},{"id":1376870,"name":"Chushu Shen","orcid":"0009-0006-5865-4754","position":2,"is_corresponding":false},{"id":730382,"name":"Hsu‐Lei Lee","orcid":"0000-0001-5509-5199","position":3,"is_corresponding":false},{"id":418398,"name":"Giselle Kaneda","orcid":"0000-0002-0547-9060","position":4,"is_corresponding":false},{"id":1122265,"name":"Jacob T. Wechsler","orcid":"0009-0003-0770-4801","position":5,"is_corresponding":false},{"id":662781,"name":"Melissa Chavez","orcid":null,"position":6,"is_corresponding":false},{"id":418397,"name":"Pablo Avalos","orcid":"0000-0002-5313-5334","position":7,"is_corresponding":false},{"id":944142,"name":"Candace L. Floyd","orcid":"0000-0003-3368-2039","position":8,"is_corresponding":false},{"id":418393,"name":"Wafa Tawackoli","orcid":"0000-0003-3742-8644","position":9,"is_corresponding":false},{"id":463629,"name":"Yibin Xie","orcid":"0000-0002-0333-567X","position":10,"is_corresponding":false},{"id":383636,"name":"Anthony Christodoulou","orcid":"0000-0002-9334-8684","position":11,"is_corresponding":false},{"id":418402,"name":"Dmitriy Sheyn","orcid":"0000-0002-3333-1485","position":12,"is_corresponding":false},{"id":289229,"name":"Debiao Li","orcid":"0000-0001-8560-8231","position":13,"is_corresponding":false},{"id":730381,"name":"Karandeep Cheema","orcid":"0009-0007-0035-1117","position":0,"is_corresponding":true}],"reference_count":16,"raw_metadata":null,"created_at":"2026-07-19T02:50:12.083054Z","pmid":"40817426","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}