Visual interpretation of [<sup>18</sup>F]Florbetaben PET supported by deep learning-based estimation of amyloid burden.

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作者： Ji-Young,Kim ^[1] ; Dongkyu,Oh ^[2] ; Kiyoung,Sung ^[1] ; Hongyoon,Choi ^[3] ; Jin Chul,Paeng ^[4] ; Gi Jeong,Cheon ^[1] ; Keon Wook,Kang ^[1] ; Dong Young,Lee ^[5] ; Dong Soo,Lee ^[6]

作者单位： Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea. ^[1] Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea. ^[2] Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea. ^[3] Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea. chy1000@snu.ac.kr. ^[4] Institute on Aging, Seoul National University, Seoul, Republic of Korea. ^[5] Radiation Medicine Institute, Seoul National University College of Medicine, Seoul, Republic of Korea. ^[6] Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea. ^[7] Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea. ^[8]

关键词 Alzheimer’s disease Amyloid PET Deep learning PET Visual quantification [18F]Florbetaben

主题词阿尔茨海默病(Alzheimer Disease);淀粉样蛋白(Amyloid);苯胺化合物(Aniline Compounds);人类(Humans);正电子发射断层显像术(Positron-Emission Tomography);二苯乙烯类(Stilbenes)

DOI 10.1007/s00259-020-05044-x

PMID 32990807

发布时间 2022-05-31

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