{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:38:30Z","timestamp":1780447110778,"version":"3.54.1"},"reference-count":30,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000968","name":"American Heart Association","doi-asserted-by":"publisher","award":["23SCISA1145192"],"award-info":[{"award-number":["23SCISA1145192"]}],"id":[{"id":"10.13039\/100000968","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016220","name":"Georgia Clinical and Translational Science Alliance","doi-asserted-by":"publisher","award":["UL1TR002345"],"award-info":[{"award-number":["UL1TR002345"]}],"id":[{"id":"10.13039\/100016220","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":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop a model\u2010driven, self\u2010supervised deep learning network for end\u2010to\u2010end simultaneous mapping of myocardial oxygen extraction fraction (mOEF) and myocardial blood volume (MBV).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>An asymmetrical spin echo\u2013prepared sequence was used to acquire mOEF and MBV images. By integrating a physical model into the training process, a self\u2010supervised learning (SSL) pattern can be regulated. A loss function consisted of the mean squared error, plus cosine similarity was used to improve the performance of network predictions for estimating mOEF and MBV simultaneously. The SSL network was trained and evaluated using simulated data with ground truths and human data in vivo from 10 healthy subjects and 10 patients with myocardial infarction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the simulation study, the SSL method demonstrated the ability of generating relatively accurate mOEF, MBV, and \u0394B maps simultaneously. In the in vivo study, healthy volunteers had an average mOEF of 0.6\u20130.7 and MBV of 0.11\u20130.13, comparable to literature\u2010reported values. In the myocardial infarction regions, the average mOEF and MBV in 5 tested patients reduced to 0.45\u2009\u00b1\u20090.09 and 0.09\u2009\u00b1\u20090.02, which were significantly lower (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) than those in normal regions (0.67\u2009\u00b1\u20090.04 and 0.13\u2009\u00b1\u20090.01, respectively).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This work has demonstrated the initial feasibility of generating mOEF and MBV maps simultaneously by a model\u2010driven, self\u2010supervised learning method.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.30555","type":"journal-article","created":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T08:07:12Z","timestamp":1746173232000},"page":"1793-1803","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Model\u2010based self\u2010supervised learning for quantitative assessment of myocardial oxygen extraction fraction and myocardial blood volume"],"prefix":"10.1002","volume":"94","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6545-2487","authenticated-orcid":false,"given":"Qi","family":"Huang","sequence":"first","affiliation":[{"name":"Mallinckrodt Institute of Radiology Washington University School of Medicine   St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoteng","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of Texas Rio Grande Valley  Edinburg Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keyan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology The First Affiliated Hospital of Zhengzhou University  Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"Li","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology Washington University School of Medicine   St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4457-0916","authenticated-orcid":false,"given":"Cihat","family":"Eldeniz","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology Washington University School of Medicine   St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalie","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology Washington University School of Medicine   St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas H.","family":"Schindler","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology Washington University School of Medicine   St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linda R.","family":"Peterson","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology Washington University School of Medicine   St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiology &amp; 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