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Med. Biol."],"published-print":{"date-parts":[[2026,8,28]]},"abstract":"Abstract<\/jats:title>\n \n Objective.<\/jats:italic>\n To address the challenges of rapid and robust quantitative MRI, particularly for\n T<\/jats:italic>\n \n 1\n \u03c1<\/jats:italic>\n <\/jats:sub>\n mapping, by developing and evaluating a novel technique\u2014spin-locked (SL) multiple overlapping echo detachment (SL-MOLED)\u2014for efficient mapping of\n T<\/jats:italic>\n \n 1\n \u03c1<\/jats:italic>\n <\/jats:sub>\n ,\n T<\/jats:italic>\n 2<\/jats:sub>\n , and\n T<\/jats:italic>\n 2<\/jats:sub>\n *<\/jats:sup>\n relaxation times with reduced sensitivity to\n B<\/jats:italic>\n 0<\/jats:sub>\n \/\n B<\/jats:italic>\n 1<\/jats:sub>\n inhomogeneities and SL \u2013related banding artifacts.\n Approach.<\/jats:italic>\n SL-MOLED integrates SL preparation into the MOLED acquisition framework, enabling simultaneous mapping of\n T<\/jats:italic>\n \n 1\n \u03c1<\/jats:italic>\n <\/jats:sub>\n ,\n T<\/jats:italic>\n 2<\/jats:sub>\n ,\n T<\/jats:italic>\n 2<\/jats:sub>\n *<\/jats:sup>\n , proton density, and estimation of \u0394\n B<\/jats:italic>\n 0<\/jats:sub>\n and\n B<\/jats:italic>\n 1<\/jats:sub>\n in approximately 11 s per slice. A synthetic data-driven deep learning reconstruction framework was trained on Bloch-simulated datasets with explicitly modeled banding artifacts, allowing effective mitigation of artifact-related errors. Validation comprised numerical experiments, phantom studies, healthy volunteer experiments, and a preliminary patient evaluation. Reconstruction accuracy was assessed using the structural similarity index measure (SSIM), mean absolute error (MAE), Pearson\u2019s correlation coefficient (\n r<\/jats:italic>\n ), and Bland\u2013Altman analysis. Short-term within-session stability and 7 d inter-session test\u2013retest repeatability were evaluated separately using ROI-based coefficients of variations.\n Main results.<\/jats:italic>\n Numerical experiments showed that networks trained with artifact modeling improved SSIM by 0.1\u20130.2 and reduced MAE by 2\u20135 ms for\n T<\/jats:italic>\n \n 1\n \u03c1<\/jats:italic>\n <\/jats:sub>\n ,\n T<\/jats:italic>\n 2<\/jats:sub>\n , and\n T<\/jats:italic>\n 2<\/jats:sub>\n *<\/jats:sup>\n compared with models trained without artifact modeling, across varying\n B<\/jats:italic>\n 0<\/jats:sub>\n \/\n B<\/jats:italic>\n 1<\/jats:sub>\n inhomogeneities and SL frequencies. Phantom studies demonstrated good agreement between SL-MOLED reconstructed maps and reference methods for\n T<\/jats:italic>\n \n 1\n \u03c1<\/jats:italic>\n <\/jats:sub>\n ,\n T<\/jats:italic>\n 2<\/jats:sub>\n , and\n T<\/jats:italic>\n 2<\/jats:sub>\n *<\/jats:sup>\n (\n r<\/jats:italic>\n > 0.997, Bland\u2013Altman bias < 3.4%).\n In vivo<\/jats:italic>\n experiments confirmed strong correlations with reference maps (\n r<\/jats:italic>\n \u2a7e 0.976) and good repeatability. In a patient with multiple sclerosis, SL-MOLED detected elevated\n T<\/jats:italic>\n \n 1\n \u03c1<\/jats:italic>\n <\/jats:sub>\n values in lesions relative to normal-appearing white matter, while\n T<\/jats:italic>\n 2<\/jats:sub>\n and\n T<\/jats:italic>\n 2<\/jats:sub>\n *<\/jats:sup>\n showed smaller changes, indicating that each parameter may reflect different underlying pathological features.\n Significance.<\/jats:italic>\n SL-MOLED provides accurate, repeatable, and artifact-robust quantitative mapping within a substantially reduced acquisition time (\u223c11 s per slice), offering a promising framework for reliable multi-parametric MRI with potential for clinical translation.\n <\/jats:p>","DOI":"10.1088\/1361-6560\/ae9a2a","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T22:53:04Z","timestamp":1786747984000},"page":"165032","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust\n T<\/i>\n \n 1\n \u03c1<\/i>\n <\/sub>\n ,\n T<\/i>\n 2<\/sub>\n , and\n T<\/i>\n 2<\/sub>\n *<\/sup>\n mapping via spin-locked MOLED with synthetic data-driven deep learning reconstruction"],"prefix":"10.1088","volume":"71","author":[{"given":"Weikun","family":"Chen","sequence":"first","affiliation":[{"name":"Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6001-6868","authenticated-orcid":true,"given":"Qing","family":"Lin","sequence":"additional","affiliation":[{"name":"Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0507-5525","authenticated-orcid":true,"given":"Taishan","family":"Kang","sequence":"additional","affiliation":[{"name":"Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University"},{"name":"Xiamen Radiological Control Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1445-2799","authenticated-orcid":true,"given":"Jian","family":"Wu","sequence":"additional","affiliation":[{"name":"Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital"},{"name":"Harvard Medical School"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0039-7040","authenticated-orcid":true,"given":"Yuchen","family":"Zheng","sequence":"additional","affiliation":[{"name":"Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfa","family":"Chen","sequence":"additional","affiliation":[{"name":"Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2767-9490","authenticated-orcid":true,"given":"Shuhui","family":"Cai","sequence":"additional","affiliation":[{"name":"Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0600-8594","authenticated-orcid":true,"given":"Congbo","family":"Cai","sequence":"additional","affiliation":[{"name":"Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,8,26]]},"reference":[{"key":"pmbae9a2abib1","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.jhep.2013.09.002","type":"journal-article","article-title":"Multiparametric magnetic resonance for the non-invasive diagnosis of liver disease","volume":"60","author":"Banerjee","year":"2014","journal-title":"J. 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