Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural Networks

Autor:	Seung Kwan, Kang, Daewoon, Kim, Seong A, Shin, Yu Kyeong, Kim, Hongyoon, Choi, Jae Sung, Lee
Rok vydání:	2022
Předmět:	Psychiatry and Mental health Cellular and Molecular Neuroscience Developmental Neuroscience Epidemiology Health Policy Radiology Nuclear Medicine and imaging Neurology (clinical) Geriatrics and Gerontology
Zdroj:	Journal of Nuclear Medicine. 64:659-666
ISSN:	2159-662X 0161-5505
DOI:	10.2967/jnumed.122.264414
Popis:	This paper proposes a novel method for the automatic quantification of amyloid positron emission tomography (PET) using the deep learning (DL)-based spatial normalization (SN) of PET images, which does not require magnetic resonance imaging (MRI) or computed tomography images of the same patient. The accuracy of the method was evaluated for three different amyloid PET radiotracers compared to MRI-parcellation-based PET quantification using FreeSurfer.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ad872aef576c3cf85412a4c19b7ae1a https://doi.org/10.2967/jnumed.122.264414 Zobrazit plný text záznamu