Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework
Abstract Our purpose in this study is to evaluate the clinical feasibility of deep-learning techniques for F-18 florbetaben (FBB) positron emission tomography (PET) image reconstruction using data acquired in a short time. We reconstructed raw FBB PET data of 294 patients acquired for 20 and 2 min i...
Enregistré dans:
Auteurs principaux: | Young Jin Jeong, Hyoung Suk Park, Ji Eun Jeong, Hyun Jin Yoon, Kiwan Jeon, Kook Cho, Do-Young Kang |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/45e7f2ba87c843b8884d08ce8c70d9f5 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Splenic uptake on FDG PET/CT correlates with Kikuchi-Fujimoto disease severity
par: Hye Seong, et autres
Publié: (2021) -
FRGAN: A Blind Face Restoration with Generative Adversarial Networks
par: Tongxin Wei, et autres
Publié: (2021) -
Image and Graph Restoration Dependent on Generative Adversarial Network Algorithm
par: Yuanhao Cao
Publié: (2021) -
Author Correction: Concordance in detecting amyloid positivity between 18F-florbetaben and 18F-flutemetamol amyloid PET using quantitative and qualitative assessments
par: Soo Hyun Cho, et autres
Publié: (2021) -
Plasma Amyloid-β Oligomerization Tendency Predicts Amyloid PET Positivity
par: Pyun JM, et autres
Publié: (2021)