{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T14:34:47Z","timestamp":1782830087430,"version":"3.54.5"},"reference-count":41,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009192","name":"Alberta Innovates","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009192","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["NMR in Biomedicine"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Clinical MRI is essential for managing neurological disorders such as multiple sclerosis (MS) but often inconsistent, limiting secondary analyses for enhanced information characterization. Our goal was to establish a new\n                    <jats:italic>Z<\/jats:italic>\n                    \u2010score template method for person\u2010specific image augmentation, as compared to a common deep learning approach termed cycle generative adversarial network (CycleGAN), and test their utility using a treatment response predicting example in MS. We examined 148 MS participants (102 females) from two cohorts, 104 and 44, respectively, with equivalent sex ratio and age, and each included T1\u2010weighted, T2\u2010weighted, and fluid attenuated inversion recovery (FLAIR) brain MRI. The 104\/148 participants were used for method development and 44\/148 for held\u2010out testing.\n                    <jats:italic>Z<\/jats:italic>\n                    \u2010score templates were constructed using different sample sizes to compare.\n                    <jats:italic>Z<\/jats:italic>\n                    scores from the best template were used to create person\u2010 and sequence\u2010specific images. Similar experiments were done using CycleGAN along with tests using the same cohort. Image quality was assessed using peak signal\u2010to\u2010noise ratio, structural similarity index, and root mean square error. Utility testing applied ResNet50\u2010based deep learning models with cycling of the typically unavailable T1\u2010weighted MRI. We found that\n                    <jats:italic>Z<\/jats:italic>\n                    \u2010score template constructed with 75 individuals was the best. Using existing images, potentially unavailable MRI could be created using either method investigated. At an individual level,\n                    <jats:italic>Z<\/jats:italic>\n                    \u2010score template synthesis was equivalent to CycleGAN results. Further, models trained with synthetic or source T1\u2010weighted images achieved similar accuracies (0.82\u20130.84\u2009\u00b1\u20090.04 vs. 0.84\u2009\u00b1\u20090.02) in tests of treatment response prediction. Without using T1\u2010weighted MRI, the model accuracy decreased to 0.72, and AUC decreased below chance. Overall,\n                    <jats:italic>Z<\/jats:italic>\n                    \u2010score template appeared to be a competitive method for person\u2010specific brain MRI augmentation, and synthesized images such as T1\u2010weighted brain MRI have the potential to support downstream applications as seen in prediction of treatment response in MS.\n                  <\/jats:p>","DOI":"10.1002\/nbm.70313","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T07:30:41Z","timestamp":1779262241000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A\n                    <i>Z<\/i>\n                    \u2010Score Template Method for Person\u2010Specific Augmentation of Clinical Brain MRI: An Investigation in Multiple Sclerosis"],"prefix":"10.1002","volume":"39","author":[{"given":"Olayinka","family":"Oladosu","sequence":"first","affiliation":[{"name":"Department of Radiology, Cumming School of Medicine University of Calgary  Calgary Alberta Canada"},{"name":"Hotchkiss Brain Institute, Cumming School of Medicine University of Calgary  Calgary Alberta Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rehman","family":"Tariq","sequence":"additional","affiliation":[{"name":"Hotchkiss Brain Institute, Cumming School of Medicine University of Calgary  Calgary Alberta Canada"},{"name":"Department of Biomedical Engineering, Schulich School of Engineering University of Calgary  Calgary Alberta Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shayan","family":"Shahrokhi","sequence":"additional","affiliation":[{"name":"Hotchkiss Brain Institute, Cumming School of Medicine University of Calgary  Calgary Alberta Canada"},{"name":"Department of Neuroscience, Cumming School of Medicine University of Calgary  Calgary Alberta Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Cumming School of Medicine University of Calgary  Calgary Alberta Canada"},{"name":"Hotchkiss Brain Institute, Cumming School of Medicine University of Calgary  Calgary Alberta Canada"},{"name":"Department of Clinical Neurosciences, Cumming School of Medicine University of Calgary  Calgary Alberta Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1097\/WCO.0000000000000559"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(23)01473-3"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.240030"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230514"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2021203786"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00398-4"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102789"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301865"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2023.103376"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108320"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2975344"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3088866"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13246-024-01443-8"},{"key":"e_1_2_11_15_1","unstructured":"R.Wu H.Wang H. 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