Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning

PURPOSE: This work attempts to decode the discriminative information in dopamine transporter (DAT) imaging using deep learning for the differential diagnosis of parkinsonism. METHODS: This study involved 1017 subjects who underwent DAT PET imaging ([(11)C]CFT) including 43 healthy subjects and 974 p...

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Detalles Bibliográficos
Autores principales: Zhao, Yu, Wu, Ping, Wu, Jianjun, Brendel, Matthias, Lu, Jiaying, Ge, Jingjie, Tang, Chunmeng, Hong, Jimin, Xu, Qian, Liu, Fengtao, Sun, Yimin, Ju, Zizhao, Lin, Huamei, Guan, Yihui, Bassetti, Claudio, Schwaiger, Markus, Huang, Sung-Cheng, Rominger, Axel, Wang, Jian, Zuo, Chuantao, Shi, Kuangyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9206631/
https://www.ncbi.nlm.nih.gov/pubmed/35588012
http://dx.doi.org/10.1007/s00259-022-05804-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9206631/
https://www.ncbi.nlm.nih.gov/pubmed/35588012
http://dx.doi.org/10.1007/s00259-022-05804-x

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