{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T15:59:12Z","timestamp":1792166352437,"version":"4.4.0"},"reference-count":58,"publisher":"IOP Publishing","issue":"20","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["12375291"],"award-info":[{"award-number":["12375291"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key Research and Development Program of China","award":["2022YFC2402102"],"award-info":[{"award-number":["2022YFC2402102"]}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"crossref","award":["2024A1515011262"],"award-info":[{"award-number":["2024A1515011262"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2025,10,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> Rapid and accurate quantitative assessment of muscle tissue characteristics is valuable for the diagnosis and monitoring of neuromuscular diseases (NMDs). Quantitative magnetic resonance imaging (MRI) enables non-invasive assessment of muscle pathology by using water <jats:italic>T<\/jats:italic>\n                  <jats:sub>2<\/jats:sub> values to assess muscle damage and proton density fat fraction (PDFF) to quantify fat infiltration. However, conventional methods for simultaneous water-fat separation and <jats:italic>T<\/jats:italic>\n                  <jats:sub>2<\/jats:sub> quantification often require long acquisition times. This study aims to develop an ultrafast method for simultaneous water-fat separation and <jats:italic>T<\/jats:italic>\n                  <jats:sub>2<\/jats:sub> quantification. <jats:italic>Approach.<\/jats:italic> A novel water-fat separation framework that combines chemical shift encoding with the multiple overlapping-echo detachment sequence (CSE-MOLED) was proposed. Synthetic training data and deep learning-based reconstruction were employed to address challenges in water-fat separation, including the complex multi-peak spectral characteristic of fat and the non-idealities in MRI acquisition. All experiments, including phantom and <jats:italic>in vivo<\/jats:italic> scans, were performed on a 3T MRI scanner. The <jats:italic>in vivo<\/jats:italic> experiments focused on the human thigh and involved five healthy volunteers, one subject with muscle atrophy, and one with muscle damage. The CSE-MOLED sequence was acquired with a spatial resolution of 1.72 mm \u00d7 1.72 mm \u00d7 5 mm. For reference, mDixon-TSE (Turbo Spin Echo) was performed to generate PDFF, water <jats:italic>T<\/jats:italic>\n                  <jats:sub>2<\/jats:sub>, and fat <jats:italic>T<\/jats:italic>\n                  <jats:sub>2<\/jats:sub> maps. <jats:italic>Main results.<\/jats:italic> In numerical experiments (<jats:italic>T<\/jats:italic>\n                  <jats:sub>2<\/jats:sub> range: water 19\u2013160 ms, fat 15\u2013160 ms; PDFF range: 14%\u2013100%), the <jats:italic>R<\/jats:italic>\n                  <jats:sup>2<\/jats:sup> values were all 0.999 for water <jats:italic>T<\/jats:italic>\n                  <jats:sub>2<\/jats:sub>, fat <jats:italic>T<\/jats:italic>\n                  <jats:sub>2<\/jats:sub>, and PDFF, with average percentage errors of 3.16%, 0.98%, and 0.78%, respectively. In phantom experiments (<jats:italic>T<\/jats:italic>\n                  <jats:sub>2<\/jats:sub> range: water 35\u2013125 ms, fat 36\u201347 ms; PDFF range: 14\u201336%), the <jats:italic>R<\/jats:italic>\n                  <jats:sup>2<\/jats:sup> values were 0.995, 0.733, and 0.996 for water <jats:italic>T<\/jats:italic>\n                  <jats:sub>2<\/jats:sub>, fat <jats:italic>T<\/jats:italic>\n                  <jats:sub>2<\/jats:sub>, and PDFF, with average percentage errors of 5.56%, 2.93%, and 1.77%, respectively. High repeatability (coefficient of variation &lt;2.0%) was achieved in both phantom and <jats:italic>in vivo<\/jats:italic> experiments. In patient scans, CSE-MOLED successfully distinguished between fat infiltration and muscle damage. <jats:italic>Significance<\/jats:italic>. CSE-MOLED simultaneously obtains <jats:italic>T<\/jats:italic>\n                  <jats:sub>2<\/jats:sub> and proton density maps for both water and fat, along with <jats:italic>T<\/jats:italic>\n                  <jats:sub>2<\/jats:sub>-corrected PDFF map, in 162 ms per slice of acquisition time, offering the potential to enhance the diagnostic accuracy of NMDs without increasing the clinical scanning burden.<\/jats:p>","DOI":"10.1088\/1361-6560\/ae0efa","type":"journal-article","created":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T22:57:06Z","timestamp":1759445826000},"page":"205015","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Fast water\/fat <i>T<\/i>\n                  <sub>2<\/sub> and PDFF mapping via multiple overlapping-echo detachment acquisition and deep learning reconstruction"],"prefix":"10.1088","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6001-6868","authenticated-orcid":true,"given":"Qing","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weikun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taishan","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1445-2799","authenticated-orcid":true,"given":"Jian","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6748-2778","authenticated-orcid":true,"given":"Xinran","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaobo","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangjie","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiazheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianzhong","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2767-9490","authenticated-orcid":true,"given":"Shuhui","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0600-8594","authenticated-orcid":true,"given":"Congbo","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"key":"pmbae0efabib1","first-page":"265","type":"conference-proceedings","article-title":"TensorFlow: a system for large-scale machine learning","author":"Abadi","year":"2016"},{"key":"pmbae0efabib2","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1002\/mrm.27786","type":"journal-article","article-title":"Separation of water and fat signal in whole\u2010body gradient echo scans using convolutional neural networks","volume":"82","author":"Andersson","year":"2019","journal-title":"Magn. 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