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Med. Biol."],"published-print":{"date-parts":[[2026,9,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Magnetic susceptibility source separation provides profound insights into tissue pathophysiology by disentangling the underlying paramagnetic and diamagnetic sources within the human brain. However, the highly time-consuming nature of existing acquisition techniques creates a critical need for rapid and reliable separation methodologies to facilitate routine clinical applications.\n                    <jats:italic>Approach.<\/jats:italic>\n                    A highly accelerated framework for whole-brain magnetic susceptibility source separation was proposed by utilizing the ultra-fast 3D-MOLED sequence. This pipeline integrates the advanced deep learning architecture to accurately map the separated susceptibility components without relying on spin echo data. To ensure quantitative stability and structural fidelity, a robust three-dimensional domain adaptation strategy was incorporated during the simulation phase. The performance of the proposed method was systematically evaluated through phantom experiments, healthy volunteer assessments, and clinical examinations of patients with multiple sclerosis.\n                    <jats:italic>Main results.<\/jats:italic>\n                    The proposed framework achieved whole-brain susceptibility source separation at 1 mm isotropic resolution with only 50 s of data acquisition. In healthy volunteers, the paramagnetic and diamagnetic susceptibility maps were highly comparable to those obtained with the conventional multi-echo gradient-echo sequence, with structural similarity indices of 0.9244 \u00b1 0.0181 and 0.9425 \u00b1 0.0186, peak signal-to-noise ratios of 33.76 \u00b1 2.37 and 37.29 \u00b1 3.51 dB, regional coefficients of determination of 0.973 and 0.934, and Bland\u2013Altman biases of \u22120.0013 and \u22120.0010 ppm, respectively. Furthermore, clinical evaluations in patients with multiple sclerosis demonstrated consistent characterization of complex lesion-related susceptibility features.\n                    <jats:italic>Significance.<\/jats:italic>\n                    Achieving whole-brain magnetic susceptibility source separation in less than one minute represents a meaningful technical advance. By substantially reducing the acquisition time required for susceptibility source separation, this accelerated framework may improve the feasibility of future clinical translation in neuroimaging workflows.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae9d11","type":"journal-article","created":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T22:54:07Z","timestamp":1787352847000},"page":"175021","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Rapid whole-brain susceptibility source separation using sub-minute three-dimensional multiple overlapping-echo detachment acquisition"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5000-9058","authenticated-orcid":true,"given":"Longkun","family":"Chen","sequence":"first","affiliation":[{"name":"Xiamen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8927-3931","authenticated-orcid":true,"given":"Qinqin","family":"Yang","sequence":"additional","affiliation":[{"name":"University of California Irvine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiwei","family":"Huang","sequence":"additional","affiliation":[{"name":"Xiamen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nuowei","family":"Ge","sequence":"additional","affiliation":[{"name":"Xiamen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9986-3066","authenticated-orcid":true,"given":"Zejun","family":"Wu","sequence":"additional","affiliation":[{"name":"Xiamen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianjun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhongshan Hospital (Xiamen) Fudan University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8281-9401","authenticated-orcid":true,"given":"Jianfeng","family":"Bao","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Zhengzhou University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8038-4056","authenticated-orcid":true,"given":"Lijun","family":"Bao","sequence":"additional","affiliation":[{"name":"Xiamen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanhao","family":"Cheng","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong Liverpool University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfa","family":"Chen","sequence":"additional","affiliation":[{"name":"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":"Xiamen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong","family":"Chen","sequence":"additional","affiliation":[{"name":"Xiamen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liuhong","family":"Zhu","sequence":"additional","affiliation":[{"name":"Zhongshan Hospital (Xiamen) Fudan 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":"Xiamen University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"pmbae9d11bib1","article-title":"Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation","author":"Alom","year":"2018","type":"preprint"},{"key":"pmbae9d11bib2","doi-asserted-by":"publisher","first-page":"3599","DOI":"10.1093\/brain\/awr278","type":"journal-article","article-title":"Tracking iron in multiple sclerosis: a combined imaging and histopathological study at 7 Tesla","volume":"134","author":"Bagnato","year":"2011","journal-title":"Brain"},{"key":"pmbae9d11bib3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102789","type":"journal-article","article-title":"SynthSeg: segmentation of brain MRI scans of any contrast and resolution without retraining","volume":"86","author":"ADNI","year":"2023","journal-title":"Med. 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