DEEPLY-SUPERVISED MULTI-DOSE PRIOR LEARNING FOR LOW-DOSE PET IMAGING

被引：1
|
作者：
Gong, Yu [1 ,3 ]
Shan, Hongming [2 ]
Teng, Yueyang [1 ]
Zheng, Hairong [3 ]
Wang, Ge [2 ]
Wang, Shanshan [3 ]
机构：
[1] NEU, Shenyang, Peoples R China
[2] RPI, Troy, NY USA
[3] Chinese Acad Sci, SIAT, Beijing, Peoples R China
来源：
2020 IEEE 17TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING WORKSHOPS (IEEE ISBI WORKSHOPS 2020) | 2020年
基金：
中国国家自然科学基金;
关键词：
Deep learning; low-dose PET; deeply-supervised; multi-dosage; NETWORK; CT;
D O I：
10.1109/isbiworkshops50223.2020.9153450
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Positron emission tomography (PET) is an advanced imaging modality for tumor staging and therapy response. However, PET radiation exposure has raised public concerns and it is in need to develop low-dose PET imaging techniques. This paper proposes to explore prior information inherited in different levels of low-dose PET images with deep learning and then utilize them to estimate high-quality PET images from the image with the lowest dose. The proposed method is evaluated on the in vivo dataset with encouraging performance.
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页数：4
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