{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T09:12:18Z","timestamp":1789463538120,"version":"build-2803163510"},"reference-count":30,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Hallym University Research Fund"},{"name":"Hallym University Medical Center Research Fund"},{"name":"Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea","award":["HR21C0198"],"award-info":[{"award-number":["HR21C0198"]}]},{"name":"Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea","award":["RS-2022-KH129707"],"award-info":[{"award-number":["RS-2022-KH129707"]}]},{"name":"Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea","award":["RS-2026-25532408"],"award-info":[{"award-number":["RS-2026-25532408"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate quantification of standardized uptake value ratio (SUVR) in amyloid PET is essential for Alzheimer's disease (AD) diagnosis but typically requires MRI-based segmentation due to subtle uptake differences between gray and white matter.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to develop and validate a 3-dimensional deep learning model capable of segmenting these tissues directly from PET images to enable MRI-free SUVR quantification for AD diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study included 385 participants who underwent brain amyloid PET and MRI. After excluding 12 data-corrupted cases, 373 subjects were divided into training (n\u2009=\u2009318) and test (n\u2009=\u200955) sets. External validation used 625 PET\/CT scans from the Alzheimer's Disease Neuroimaging Initiative. Model performance was assessed using Dice coefficients and intersection over union. PET-based SUVRs derived from model-generated masks were compared with MRI-based SUVRs using Spearman correlation, and their diagnostic utility was evaluated by group differences and receiver operating characteristic analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The model achieved high Dice coefficients for grey matter (GM; 0.785 internal, 0.743 external) and white matter (WM; 0.838 internal, 0.803 external). PET\/CT-based SUVR values strongly correlated with MRI references (Spearman's \u03c1\u2009\u2265\u20090.98, p\u2009&lt;\u20090.001). PET\/CT-derived SUVR\n                      <jats:sub>GM<\/jats:sub>\n                      and SUVR\n                      <jats:sub>GM\/WM<\/jats:sub>\n                      predicted amyloid status (AUC 0.86 and 0.85, respectively) and cognitive impairment (AUC 0.78).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Deep learning\u2013based amyloid PET segmentation enables accurate MRI-free quantification of gray and white matter SUVRs. This approach simplifies clinical workflow while maintaining diagnostic performance comparable to MRI-based methods for AD diagnosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877261470021","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T13:39:07Z","timestamp":1784813947000},"page":"650-660","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["MRI-free amyloid PET quantification using a deep learning model and white matter reference"],"prefix":"10.1177","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5624-1821","authenticated-orcid":false,"given":"Seung-Ho","family":"Shin","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Hallym University Chuncheon Sacred Heart Hospital, Chuncheon-si, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0538-4037","authenticated-orcid":false,"given":"Yongcheol","family":"Mo","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Center, Hallym University Sacred Heart Hospital, Chuncheon-si, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hee Sung","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, College of Medicine, Hallym University, Hallym University Sacred Heart Hospital, Anyang-si, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minwoo","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Neurology, Hallym University Sacred Heart Hospital, Anyang-si, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Jun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology and Pain Medicine, Hallym University College of Medicine, Chuncheon-si, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8762-8340","authenticated-orcid":false,"given":"Chulho","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Neurology, Hallym University College of Medicine, Chuncheon-si, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2003-813X","authenticated-orcid":false,"given":"Hyung Jin","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, Hallym University College of Medicine, Hallym University Chuncheon Sacred Heart Hospital, Chuncheon, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,7,23]]},"reference":[{"key":"e_1_3_3_2_2","volume-title":"A blueprint for dementia research","author":"World Health Organization","year":"2022","unstructured":"World Health Organization. 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