Validation of deep learning-based nonspecific estimates for amyloid burden quantification with longitudinal data.

Nai, Ying-Hwey; Liu, Haohui; Reilhac, Anthonin

Nai, Ying-Hwey; Liu, Haohui; Reilhac, Anthonin.

Affiliation

Nai YH; Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. Electronic address: mednyh@nus.edu.sg.
Liu H; Carnegie Mellon University, Pennsylvania, United States.
Reilhac A; Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

Phys Med ; 99: 85-93, 2022 Jul.

Article in En | MEDLINE | ID: mdl-35665624

Subject(s)

Alzheimer Disease; Cognitive Dysfunction; Deep Learning; Alzheimer Disease/diagnostic imaging; Amyloid beta-Peptides/metabolism; Brain/metabolism; Humans; Positron-Emission Tomography/methods

Key words

Alzheimer's disease; Amyloid-Beta Positron Emission Tomography (Aß-PET); Deep learning; Nonspecific uptake

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Alzheimer Disease / Cognitive Dysfunction / Deep Learning Type of study: Clinical_trials / Risk_factors_studies Limits: Humans Language: En Journal: Phys Med Journal subject: BIOFISICA / BIOLOGIA / MEDICINA Year: 2022 Type: Article

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