{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T10:43:30Z","timestamp":1768214610537,"version":"3.49.0"},"reference-count":35,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["114\u20102628\u2010E\u2010002\u2010019\u2010MY3"],"award-info":[{"award-number":["114\u20102628\u2010E\u2010002\u2010019\u2010MY3"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004737","name":"National Health Research Institutes","doi-asserted-by":"publisher","award":["NHRI\u2010EX114\u2010 11205EC"],"award-info":[{"award-number":["NHRI\u2010EX114\u2010 11205EC"]}],"id":[{"id":"10.13039\/501100004737","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Amyloid PET imaging has been extensively employed in the noninvasive assessment of amyloid\u2010beta accumulation in Alzheimer's disease. Various amyloid radiotracers are commonly used in clinical settings; however, the limited interchangeability among these radiotracers hinders the feasibility of long\u2010term clinical trials and multicenter comparisons. The Centiloid method was proposed for standardization, though providing a single score per image; voxel\u2010wise translation remains a formidable task.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This paper proposes a U\u2010Net model based on a deformable convolution network (DCNv3\u2010based U\u2010Net) for []\u2010Pittsburgh compound B\u2010to\u2010[]\u2010florbetaben image translation to augment existing datasets for large\u2010scale model training and provide image information when inconsistencies between visual assessments and the Centiloid scale\u00a0occur.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The DCNv3\u2010based U\u2010Net combined the benefits of deformable convolution that captures long\u2010range dependencies with efficient computation and the encoder\u2013decoder architecture with skip connections for local\u2013global feature learning and image\u00a0synthesis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The prediction images presented increased homogeneity to other previous models, closely resembling the texture of []\u2010florbetaben.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The DCNv3\u2010based U\u2010Net demonstrated high performance in metrics measurement and statistical analyses for the PET image\u2010to\u2010image translation task. This work also justified the importance of MR images in providing structural information.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70168","type":"journal-article","created":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T04:46:47Z","timestamp":1764305207000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Paired PET\u2010MRI Deep Learning Model for Translating [\n                    <sup>11<\/sup>\n                    C]PiB to [\n                    <sup>18<\/sup>\n                    F]Florbetaben Amyloid Images"],"prefix":"10.1002","volume":"52","author":[{"given":"Cheng\u2010Han","family":"Tsai","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering National Taiwan University Taipei Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shao\u2010Yi","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging and Radiological Sciences Chang Gung University Taoyuan Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu\u2010Nong","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering National Taiwan University Taipei Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han\u2010Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering National Taiwan University Taipei Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ing\u2010Tsung","family":"Hsiao","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging and Radiological Sciences Chang Gung University Taoyuan Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin T.","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering National Taiwan University Taipei Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.37765\/ajmc.2020.88482"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41572-021-00269-y"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2018.02.018"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/alz.13859"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/ana.20009"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(08)70001-2"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2018.06.1353"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13550-018-0456-7"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature19323"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2023.13239"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2212948"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.121.263195"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2014.07.003"},{"key":"e_1_2_9_15_1","doi-asserted-by":"crossref","unstructured":"EvansA CollinsD MillsS BrownE KellyR PetersT.3D statistical neuroanatomical models from 305 MRI volumes. 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