{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T04:12:22Z","timestamp":1773029542478,"version":"3.50.1"},"reference-count":50,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:00:00Z","timestamp":1767139200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:00:00Z","timestamp":1767139200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFA1607502"],"award-info":[{"award-number":["2023YFA1607502"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12375291"],"award-info":[{"award-number":["12375291"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>\n                      To propose a gradient\u2010echo multiple overlapping\u2010echo detachment (GRE\u2010MOLED) method for rapid abdominal T\n                      <jats:sub>2<\/jats:sub>\n                      * mapping, and to systematically validate its efficacy in non\u2010invasive monitoring of dynamic hepatic glycometabolism.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The GRE\u2010MOLED sequence was optimized to achieve ultrafast abdominal T\n                      <jats:sub>2<\/jats:sub>\n                      * mapping under free\u2010breathing without respiratory gating, the scan time per slice was only 60\u2009ms. A deep neural network architecture was developed and trained on synthetic data generated through Bloch equation simulations to achieve direct end\u2010to\u2010end reconstruction of T\n                      <jats:sub>2<\/jats:sub>\n                      * maps from GRE\u2010MOLED images. The proposed method was evaluated through numerical abdomen experiments, water phantom experiments, test\u2013retest experiments in healthy volunteers, and dynamic glycometabolic experiments.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Quantitative analyses revealed excellent agreement between the results of our method and reference method. Numerical abdomen experiment achieved mean structural similarity index (SSIM) of 0.9818 and voxel\u2010wise\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.9163. Water phantom experiments demonstrated high linear correlation (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.9992) with a Bland\u2013Altman bias of 0.2686\u2009ms (95% limits of agreement: \u22121.1048 to 1.6420\u2009ms). Test\u2013retest analysis showed intra\u2010subject coefficient of variation (CV) below 7% across multiple organs. Dynamic glycometabolism mapping successfully captured postprandial hepatic T\n                      <jats:sub>2<\/jats:sub>\n                      * fluctuations with temporal resolution outperforming conventional breath\u2010holding methods.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      The GRE\u2010MOLED method enables robust abdominal T\n                      <jats:sub>2<\/jats:sub>\n                      * mapping under free\u2010breathing, demonstrating obvious potential for non\u2010invasive assessment of hepatic glycometabolic dynamics. Validation across numerical simulations, water phantom and in vivo experiments confirm technical reliability and high reproducibility.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70247","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T08:50:11Z","timestamp":1767171011000},"page":"2644-2657","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Instantaneous Abdominal\n                    <scp>\n                      T\n                      <sub>2<\/sub>\n                    <\/scp>\n                    * Mapping via Single\u2010Shot\n                    <scp>MOLED<\/scp>\n                    Under Free\u2010Breathing: A Preliminary Study of Hepatic Glycometabolism Imaging"],"prefix":"10.1002","volume":"95","author":[{"given":"Ping","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Electronic Science, Fujian Provincial 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