{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T03:58:08Z","timestamp":1790654288863,"version":"4.1.0"},"reference-count":44,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MR\/T046473\/1"],"award-info":[{"award-number":["MR\/T046473\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"publisher","award":["C44767\/A29458"],"award-info":[{"award-number":["C44767\/A29458"]}],"id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/V034537\/1"],"award-info":[{"award-number":["EP\/V034537\/1"]}],"id":[{"id":"10.13039\/501100000266","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":[[2026,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Altered placental capillary blood flow is closely linked to obstetric complications, yet quantifying capillary\u2010scale blood velocity remains challenging with existing imaging methods. This is partially because capillary networks form disordered microvascular beds at the voxel scale, rather than coherent, directional vessels. Here, we combine Monte Carlo (MC) simulations with machine learning to estimate placental capillary blood velocity directly from diffusion MRI (dMRI) data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>MC simulations incorporating perfusion and diffusion were performed to generate signal dictionaries for supervised machine learning regressors (random forest [RF] and multilayer perceptron [MLP]) to estimate velocity, perfusion fraction and diffusivity. The trained regressors were applied to simulated and in vivo dMRI data, with intravoxel incoherent motion (IVIM)\u2010based estimates as baselines for comparison.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our approach outperformed IVIM\u2010based methods on simulated test data in recovering ground\u2010truth parameters. Among the evaluated models, the MC\u2010based MLP approach produced physiologically reasonable estimates of velocity in in vivo placentas, in agreement with previously reported measures and exhibited greater sensitivity than IVIM\u2010generated methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study presents an approach to characterize capillary perfusion that alternates conventional IVIM models, particularly in organs such as the placenta, where pseudo\u2010diffusion coefficient can be similar to tissue diffusion coefficient, limiting reliable IVIM parameter separation. Our method could enable detection of capillary blood\u2010flow alterations, which could help identify pathological placental blood\u2010flow conditions and potentially diagnosis relevant diseases, such as preeclampsia and fetal growth restriction. More broadly, the approach may translate to other organs where microscale blood\u2010flow changes are key indicators of underlying pathologies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70551","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T04:35:19Z","timestamp":1786422919000},"page":"2967-2977","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Placental Blood\u2010Flow Velocity Quantification From Diffusion\n                    <scp>MRI<\/scp>"],"prefix":"10.1002","volume":"96","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9761-573X","authenticated-orcid":false,"given":"ZhuangJian","family":"Yang","sequence":"first","affiliation":[{"name":"Hawkes Institute and Department of Computer Science University College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diana Cruz 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