{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T18:59:09Z","timestamp":1785178749663,"version":"3.55.0"},"reference-count":47,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"8","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Stroke"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>Magnetic resonance imaging (MRI) is critical for acute stroke triage, but it is time-consuming and often requires contrast injection for perfusion imaging. This study aimed to synthesize T-map perfusion maps from routinely available, noncontrast diffusion-weighted imaging and fluid-attenuated inversion recovery using deep generative models. We hypothesized that relevant perfusion information could be inferred from these modalities to streamline imaging and reduce reliance on dynamic susceptibility contrast perfusion.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>Acute magnetic resonance imaging data from 355 patients with anterior circulation stroke, including dynamic susceptibility contrast perfusion, were retrospectively collected from 2 European centers (Heidelberg: 2010\u20132018; Bordeaux: 2021\u20132022). Six versions of a denoising diffusion probabilistic model and a generative adversarial network architecture were trained to generate synthetic time-to-maximum (T-max) perfusion maps from diffusion-weighted imaging, fluid-attenuated inversion recovery, and infarct core mask as inputs. Performance was assessed by comparing synthetic and ground-truth T-max maps using image similarity metrics. Regions with T-max &gt;6 s were compared using Dice coefficients, and mismatch volume distributions were analyzed. An ablation study quantified the contribution of each input.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>The best performance was achieved by a denoising diffusion probabilistic model with a 2.5-dimensional architecture using diffusion-weighted imaging, fluid-attenuated inversion recovery, infarct core mask, and a perfusion-weighted loss function. It produced synthetic perfusion T-max maps with high similarity to ground truth under 110 s. The model showed strong spatial overlap for T-max&gt; 6 s regions in internal validation (average Dice, 0.82; SD, 0.08) and external validation (average Dice, 0.59; SD, 0.13), respectively. Synthetic maps closely matched ground truth mismatch distributions, capturing key perfusion patterns. The infarct core mask played a critical role in model performance, alongside diffusion-weighted imaging and fluid-attenuated inversion recovery inputs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>We propose a noninvasive, scalable framework to generate synthetic T-max perfusion maps from noncontrast magnetic resonance imaging. This approach could expand access to perfusion data in acute stroke, shorten imaging protocols, and accelerate treatment decisions by eliminating the need for contrast-enhanced acquisition.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/strokeaha.126.055813","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T09:00:27Z","timestamp":1781773227000},"page":"2375-2387","update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Generating Synthetic MR Perfusion Maps From DWI and FLAIR in Acute Ischemic Stroke: Development and External Validation of a Deep Learning Model"],"prefix":"10.1161","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3699-7271","authenticated-orcid":true,"given":"Anna","family":"Matsulevits","sequence":"first","affiliation":[{"name":"Groupe d\u2019Imagerie Neurofonctionnelle, Institut des Maladies Neurod\u00e9g\u00e9n\u00e9ratives- UMR 5293, CNRS, CEA, University of Bordeaux, France (A.M., M.T.S.)."},{"name":"Brain Connectivity and Behaviour Laboratory, Bordeaux University, France (A.M., M.T.S., T.T.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0255-9724","authenticated-orcid":true,"given":"Alexander","family":"Koch","sequence":"additional","affiliation":[{"name":"CLAIM\u2013Charit\u00e9 Lab for AI in Medicine, Charit\u00e9\u2013Universit\u00e4tsmedizin Berlin, corporate member of Freie Universit\u00e4t Berlin and Humboldt-Universit\u00e4t zu Berlin, Germany (A.K., A.H., O.U.A., D.F.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clara","family":"Mah\u00e9-Verdure","sequence":"additional","affiliation":[{"name":"Centre Hospitalier Universitaire (CHU) de Bordeaux, Neuroimagerie Diagnostique et Th\u00e9rapeutique, France (C.M.-V., G.M., T.T.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Bendszus","sequence":"additional","affiliation":[{"name":"Department of Neuroradiology, University Hospital Heidelberg, Germany (M. Bendszus)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3447-5453","authenticated-orcid":true,"given":"Adam","family":"Hilbert","sequence":"additional","affiliation":[{"name":"CLAIM\u2013Charit\u00e9 Lab for AI in Medicine, Charit\u00e9\u2013Universit\u00e4tsmedizin Berlin, corporate member of Freie Universit\u00e4t Berlin and Humboldt-Universit\u00e4t zu Berlin, Germany (A.K., A.H., O.U.A., D.F.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2979-6507","authenticated-orcid":true,"given":"Mary","family":"Boullet","sequence":"additional","affiliation":[{"name":"Centre Hospitalier Universitaire (CHU) de Bordeaux, Neurologie Vasculaire, France (M. Boullet, S.O., I.S.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7611-7753","authenticated-orcid":true,"given":"Gaultier","family":"Marnat","sequence":"additional","affiliation":[{"name":"Centre Hospitalier Universitaire (CHU) de Bordeaux, Neuroimagerie Diagnostique et Th\u00e9rapeutique, France (C.M.-V., G.M., T.T.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2101-823X","authenticated-orcid":true,"given":"Matthias A.","family":"Mutke","sequence":"additional","affiliation":[{"name":"Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel (M.A.M.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Orhun Utku","family":"Aydin","sequence":"additional","affiliation":[{"name":"CLAIM\u2013Charit\u00e9 Lab for AI in Medicine, Charit\u00e9\u2013Universit\u00e4tsmedizin Berlin, corporate member of Freie Universit\u00e4t Berlin and Humboldt-Universit\u00e4t zu Berlin, Germany (A.K., A.H., O.U.A., D.F.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6710-8153","authenticated-orcid":true,"given":"St\u00e9phane","family":"Olindo","sequence":"additional","affiliation":[{"name":"Centre Hospitalier Universitaire (CHU) de Bordeaux, Neurologie Vasculaire, France (M. Boullet, S.O., I.S.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1171-4215","authenticated-orcid":true,"given":"Igor","family":"Sibon","sequence":"additional","affiliation":[{"name":"Centre Hospitalier Universitaire (CHU) de Bordeaux, Neurologie Vasculaire, France (M. Boullet, S.O., I.S.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0329-1814","authenticated-orcid":true,"given":"Michel","family":"Thiebaut de Schotten","sequence":"additional","affiliation":[{"name":"Groupe d\u2019Imagerie Neurofonctionnelle, Institut des Maladies Neurod\u00e9g\u00e9n\u00e9ratives- UMR 5293, CNRS, CEA, University of Bordeaux, France (A.M., M.T.S.)."},{"name":"Brain Connectivity and Behaviour Laboratory, Bordeaux University, France (A.M., M.T.S., T.T.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5407-2331","authenticated-orcid":true,"given":"Dietmar","family":"Frey","sequence":"additional","affiliation":[{"name":"CLAIM\u2013Charit\u00e9 Lab for AI in Medicine, Charit\u00e9\u2013Universit\u00e4tsmedizin Berlin, corporate member of Freie Universit\u00e4t Berlin and Humboldt-Universit\u00e4t zu Berlin, Germany (A.K., A.H., O.U.A., D.F.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7151-6325","authenticated-orcid":true,"given":"Thomas","family":"Tourdias","sequence":"additional","affiliation":[{"name":"Brain Connectivity and Behaviour Laboratory, Bordeaux University, France (A.M., M.T.S., T.T.)."},{"name":"Centre Hospitalier Universitaire (CHU) de Bordeaux, Neuroimagerie Diagnostique et Th\u00e9rapeutique, France (C.M.-V., G.M., T.T.)."},{"name":"Universit\u00e9 de Bordeaux, Neurocentre Magendie, INSERM U1215, France (T.T.)."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.123.045154"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1804355"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1604267"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.118.023882"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.114.005305"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1813046"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(19)31053-0"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1713973"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1136\/jnis-2023-021366"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.diii.2024.11.002"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101552"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.211394"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.diii.2025.03.004"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2024.120770"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.12112430"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A4088"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06094-y"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01875-5"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","unstructured":"Henry J Natalie T Madsen D. Pix2Pix GAN for image-to-image translation. 2021. doi: 10.13140\/RG.2.2.32286.66887","DOI":"10.13140\/RG.2.2.32286.66887"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.108291"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1161\/hs1101.098331"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2016150789"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamaneurol.2021.2319"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2118191"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2213379"},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2214403"},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(23)02032-9"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2314063"},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.1161\/STR.0000000000000513"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1136\/neurintsurg-2018-014569"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.125.042299"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.122.039182"},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamaneurol.2022.4714"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1002\/ana.26925"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1097\/WCO.0000000000001235"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-39278-0"},{"key":"e_1_3_3_38_2","first-page":"1331","article-title":"Diffusion-weighted MR imaging in acute ischemia: value of apparent diffusion coefficient and signal intensity thresholds in predicting tissue at risk and final infarct size.","volume":"25","author":"Na DG","year":"2004","unstructured":"Na DG, Thijs VN, Albers GW, Moseley ME, Marks MP. Diffusion-weighted MR imaging in acute ischemia: value of apparent diffusion coefficient and signal intensity thresholds in predicting tissue at risk and final infarct size. AJNR Am J Neuroradiol. 2004;25:1331\u20131336.","journal-title":"AJNR Am J Neuroradiol"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.3171\/jns.2001.95.3.0450"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2493080107"},{"key":"e_1_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1312"},{"key":"e_1_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12938-015-0064-y"},{"key":"e_1_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2024.100340"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2024.108200"},{"key":"e_1_3_3_45_2","doi-asserted-by":"publisher","DOI":"10.1093\/bjrai\/ubae013"},{"key":"e_1_3_3_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2014.2325029"},{"key":"e_1_3_3_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocn.2021.09.025"},{"key":"e_1_3_3_48_2","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.119.028337"}],"container-title":["Stroke"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ahajournals.org\/doi\/full\/10.1161\/STROKEAHA.126.055813","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T18:02:52Z","timestamp":1785175372000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ahajournals.org\/doi\/10.1161\/STROKEAHA.126.055813"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":47,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["10.1161\/STROKEAHA.126.055813"],"URL":"https:\/\/doi.org\/10.1161\/strokeaha.126.055813","relation":{},"ISSN":["0039-2499","1524-4628"],"issn-type":[{"value":"0039-2499","type":"print"},{"value":"1524-4628","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"2025-10-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-20","order":1,"name":"revised","label":"Revised","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}