{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T22:52:42Z","timestamp":1761864762626,"version":"build-2065373602"},"reference-count":36,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:00:00Z","timestamp":1744848000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["2022093"],"award-info":[{"award-number":["2022093"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82271985"],"award-info":[{"award-number":["82271985"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["NMR in Biomedicine"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>The pathological changes in deep medullary veins (DMVs) have been reported in various diseases. However, accurate modeling and quantification of DMVs remain challenging. We aim to propose and assess an automated approach for modeling and quantifying DMVs at 7 Tesla (7\u2009T) MRI. A multi\u2010echo\u2010input Res\u2010Net was developed for vascular segmentation, and a minimum path loss function was used for modeling and quantifying the geometric parameter of DMVs. Twenty\u2010one patients diagnosed as subcortical vascular dementia (SVaD) and 20 condition matched controls were included in this study. The amplitude and phase images of gradient echo with five echoes were acquired at 7\u2009T. Ten GRE images were manually labeled by two neurologists and compared with the results obtained by our proposed method. Independent samples <jats:italic>t<\/jats:italic> test and Pearson correlation were used for statistical analysis in our study, and <jats:italic>p<\/jats:italic> value &lt; 0.05 was considered significant. No significant offset was found in centerlines obtained by human labeling and our algorithm (<jats:italic>p<\/jats:italic>\u2009=\u20090.734). The length difference between the proposed method and manual labeling was smaller than the error between different clinicians (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). Patients with SVaD exhibited fewer DMVs (mean difference\u2009=\u2009\u221260.710\u2009\u00b1\u200921.810, <jats:italic>p<\/jats:italic>\u2009=\u20090.011) and higher curvature (mean difference\u2009=\u20090.12\u2009\u00b1\u20090.022, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.0001), corresponding to their higher Vascular Dementia Assessment Scale\u2010Cog (VaDAS\u2010Cog) scores (mean difference\u2009=\u20094.332\u2009\u00b1\u20091.992, <jats:italic>p<\/jats:italic>\u2009=\u20090.036) and lower Mini\u2010Mental State Examination (MMSE) (mean difference\u2009=\u2009\u22123.071\u2009\u00b1\u20091.443, <jats:italic>p<\/jats:italic>\u2009=\u20090.047). The MMSE scores were positively correlated with the numbers of DMVs (<jats:italic>r<\/jats:italic>\u2009=\u20090.437, <jats:italic>p<\/jats:italic>\u2009=\u20090.037) and were negatively correlated with the curvature (<jats:italic>r<\/jats:italic>\u2009=\u2009\u22120.426, <jats:italic>p<\/jats:italic>\u2009=\u20090.042). In summary, we proposed a novel framework for automated quantifying the morphologic parameters of DMVs. These characteristics of DMVs are expected to help the research and diagnosis of cerebral small vessel diseases with DMV lesions.<\/jats:p>","DOI":"10.1002\/nbm.70042","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T04:47:16Z","timestamp":1744865236000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Res\u2010Net\u2010Based Modeling and Morphologic Analysis of Deep Medullary Veins Using Multi\u2010Echo GRE at 7\u2009T MRI"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5299-5460","authenticated-orcid":false,"given":"Zhixin","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Cognitive Science and Mental Health Institute of Biophysics, Chinese Academy of Sciences  Beijing China"},{"name":"Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence, Hefei Comprehensive National Science Center  Hefei China"},{"name":"University of Chinese Academy of Sciences  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Liang","sequence":"additional","affiliation":[{"name":"Navy Clinical College, The Fifth School of Clinical Medicine Anhui Medical University  Hefei Anhui China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Science and Mental Health Institute of Biophysics, Chinese Academy of Sciences  Beijing China"},{"name":"University of Chinese Academy of Sciences  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueyi","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Medicine South China University of Technology  Guangzhou Guangdong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yishuang","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Science and Mental Health Institute of Biophysics, Chinese Academy of Sciences  Beijing China"},{"name":"University of Chinese Academy of Sciences  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Yang","sequence":"additional","affiliation":[{"name":"Navy Clinical College, The Fifth School of Clinical Medicine Anhui Medical University  Hefei Anhui China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianyao","family":"Wang","sequence":"additional","affiliation":[{"name":"Navy Clinical College, The Fifth School of Clinical Medicine Anhui Medical University  Hefei Anhui China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"An","sequence":"additional","affiliation":[{"name":"Siemens Shenzhen Magnetic Resonance Ltd.  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