{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T01:02:32Z","timestamp":1791334952250,"version":"4.3.1"},"reference-count":37,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T00:00:00Z","timestamp":1741564800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100023312","name":"Wellcome EPSRC Centre for Medical Engineering","doi-asserted-by":"publisher","award":["WT203148\/Z\/16\/Z"],"award-info":[{"award-number":["WT203148\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/501100023312","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018645","name":"Musculoskeletal Research Center, Washington University in St. Louis","doi-asserted-by":"publisher","award":["MR\/W019469\/1"],"award-info":[{"award-number":["MR\/W019469\/1"]}],"id":[{"id":"10.13039\/100018645","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018645","name":"Musculoskeletal Research Center, Washington University in St. Louis","doi-asserted-by":"publisher","award":["MR\/X010007\/1"],"award-info":[{"award-number":["MR\/X010007\/1"]}],"id":[{"id":"10.13039\/100018645","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":[[2025,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To provide real\u2010time, organ\u2010specific quantitative information \u2014 specifically placental and fetal brain T2 * \u2014 to optimize and personalize fetal MRI examinations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A low\u2010latency setup enables real\u2010time processing, including segmentation, T2* fitting, and centile calculation. Two nnU\u2010Nets were trained on 2\u2009989 fetal brains, and 540 placental datasets for automatic segmentation. Normative T2* curves over gestation were derived from 88 healthy cases. Prospective testing included 50 fetal MRI scans: A validation cohort (10 exams with three intra\u2010scan repetitions) and an evaluation cohort (40 participants). Validation was performed with Bland\u2010Altman assessments and Dice coefficients between repetitions, manual\/automatic segmentations, and online\/offline quantification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>T2* maps and centiles for the fetal brain and placenta were available in under one minute for all cases. The validation cohort showed robust reproducibility, with intra\u2010scan mean T2* differences of 1.04, \u22123.17, and 5.07\u00a0ms for the fetal brain and \u22123.15, 4.74, and 2.45\u00a0ms for the placenta. Mean T2* differences between online and offline processing were 1.63\u00a0ms and 0.16\u00a0ms for the fetal brain and placenta, respectively. Dice coefficients were  for the placenta and  for the fetal brain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Real\u2010time quantitative imaging supports personalized MR exams, optimizing sequence selection and working towards reducing recall rates. The ability to assess T2*, a potential biomarker for pregnancy complications, in real\u2010time opens new clinical possibilities. Future research will apply this pipeline to pregnancies affected by preeclampsia and growth restriction and explore MR\u2010guided fetal interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.30497","type":"journal-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T00:55:31Z","timestamp":1741740931000},"page":"615-624","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Real\u2010time fetal brain and placental T2* mapping at 0.55T MRI"],"prefix":"10.1002","volume":"94","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7575-4244","authenticated-orcid":false,"given":"Jordina","family":"Aviles Verdera","sequence":"first","affiliation":[{"name":"Early Life Imaging Department School of Biomedical Engineering and Imaging Sciences, King's College London  London UK"},{"name":"Imaging Physics and Engineering Department School of Biomedical Engineering and Imaging Sciences, King's College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7520-081X","authenticated-orcid":false,"given":"Sara","family":"Neves Silva","sequence":"additional","affiliation":[{"name":"Early Life Imaging Department School of Biomedical Engineering and Imaging Sciences, King's College London  London UK"},{"name":"Imaging Physics and Engineering Department School of Biomedical Engineering and Imaging Sciences, King's College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7041-0150","authenticated-orcid":false,"given":"Kelly M.","family":"Payette","sequence":"additional","affiliation":[{"name":"Early Life Imaging Department School of Biomedical Engineering and Imaging Sciences, King's College London  London UK"},{"name":"Imaging Physics and Engineering Department School of Biomedical Engineering and Imaging Sciences, King's College London  London 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UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8925-9032","authenticated-orcid":false,"given":"Shaihan J.","family":"Malik","sequence":"additional","affiliation":[{"name":"Early Life Imaging Department School of Biomedical Engineering and Imaging Sciences, King's College London  London UK"},{"name":"Imaging Physics and Engineering Department School of Biomedical Engineering and Imaging Sciences, King's College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph V.","family":"Hajnal","sequence":"additional","affiliation":[{"name":"Early Life Imaging Department School of Biomedical Engineering and Imaging Sciences, King's College London  London UK"},{"name":"Imaging Physics and Engineering Department School of Biomedical Engineering and Imaging Sciences, King's College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary A.","family":"Rutherford","sequence":"additional","affiliation":[{"name":"Early Life Imaging Department School of Biomedical Engineering and Imaging Sciences, King's College London  London UK"},{"name":"Imaging Physics and Engineering Department School of Biomedical Engineering and Imaging Sciences, King's College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3476-3500","authenticated-orcid":false,"given":"Jana","family":"Hutter","sequence":"additional","affiliation":[{"name":"Early Life Imaging Department School of Biomedical Engineering and Imaging Sciences, King's College London  London UK"},{"name":"Imaging Physics and Engineering Department School of Biomedical Engineering and Imaging Sciences, King's College London  London UK"},{"name":"Smart Imaging Lab Radiological Institute, University Hospital Erlangen  Erlangen 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