Work

Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI

Year: 2017

Type: article

Source: Neurocomputing

DOI: 10.1016/j.neucom.2017.06.048

Authors: Lei Xiang, Yu Qiao, Dong Nie, Le An, Weili Lin, Qian Wang, Dinggang Shen

Institutions: Shanghai Jiao Tong University, Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, University of North Carolina at Chapel Hill, Korea University

Language: English

Topic: Medical Imaging Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Natural Science Foundation of China, Ministry of Science and Technology of the People's Republic of China, Science and Technology Commission of Shanghai Municipality, National Institutes of Health, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering

Awards: G1666955300, G3712795371, Automatic Pelvic Organ Delineation in Prostate Cancer Treatment, G4739438220, Longitudinal Mapping of Human Brain Development in the First Years of Life, G6908867551, Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI

OpenAlex ID: W2729145866

FWCI: 18.25

Cites: 61

Cited by: 311

Related to: 10