{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T15:23:12Z","timestamp":1747149792087,"version":"3.40.5"},"reference-count":40,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T00:00:00Z","timestamp":1655769600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100019266","name":"Korea Medical Device Development Fund","doi-asserted-by":"publisher","award":["9991006735"],"award-info":[{"award-number":["9991006735"]}],"id":[{"id":"10.13039\/100019266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF\u20102020R1A4A1018714","NRF\u20102020R1A2C2008949"],"award-info":[{"award-number":["NRF\u20102020R1A4A1018714","NRF\u20102020R1A2C2008949"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>A typical stroke MRI protocol includes perfusion\u2010weighted imaging (PWI) and MR angiography (MRA), requiring a second dose of contrast agent. A deep learning method to acquire both PWI and MRA with single dose can resolve this issue.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To acquire both PWI and MRA simultaneously using deep learning approaches.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study type<\/jats:title><jats:p>Retrospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Subjects<\/jats:title><jats:p>A total of 60 patients (30\u201373\u2009years old, 31 females) with ischemic symptoms due to occlusion or \u226550% stenosis (measured relative to proximal artery diameter) of the internal carotid artery, middle cerebral artery, or anterior cerebral artery. The 51\/1\/8 patient data were used as training\/validation\/test.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>A 3\u2009T, time\u2010resolved angiography with stochastic trajectory (contrast\u2010enhanced MRA) and echo planar imaging (dynamic susceptibility contrast MRI, DSC\u2010MRI).<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>We investigated eight different U\u2010Net architectures with different encoder\/decoder sizes and with\/without an adversarial network to generate perfusion maps from contrast\u2010enhanced MRA. Relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), mean transit time (MTT), and time\u2010to\u2010max (T<jats:sub>max<\/jats:sub>) were mapped from DSC\u2010MRI and used as ground truth to train the networks and to generate the perfusion maps from the contrast\u2010enhanced MRA input.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Normalized root mean square error, structural similarity (SSIM), peak signal\u2010to\u2010noise ratio (pSNR), DICE, and FID scores were calculated between the perfusion maps from DSC\u2010MRI and contrast\u2010enhanced MRA. One\u2010tailed <jats:italic>t<\/jats:italic>\u2010test was performed to check the significance of the improvements between networks. <jats:italic>P<\/jats:italic> values\u2009&lt;\u20090.05 were considered significant.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The four perfusion maps were successfully extracted using the deep learning networks. U\u2010net with multiple decoders and enhanced encoders showed the best performance (pSNR 24.7\u2009\u00b1\u20093.2 and SSIM 0.89\u2009\u00b1\u20090.08 for rCBV). DICE score in hypo\u2010perfused area showed strong agreement between the generated perfusion maps and the ground truth (highest DICE: 0.95\u2009\u00b1\u20090.04).<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>With the proposed approach, dynamic angiography MRI may provide vessel architecture and perfusion\u2010relevant parameters simultaneously from a single scan.<\/jats:p><\/jats:sec><jats:sec><jats:title>Evidence Level<\/jats:title><jats:p>3<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 5<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.28315","type":"journal-article","created":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T09:20:40Z","timestamp":1655803240000},"page":"456-469","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Perfusion Maps Acquired From Dynamic Angiography <scp>MRI<\/scp> Using Deep Learning 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