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Self-supervised deep learning for joint 3D low-dose PET/CT image denoising.
Zhao, Feixiang; Li, Dongfen; Luo, Rui; Liu, Mingzhe; Jiang, Xin; Hu, Junjie.
Affiliation
  • Zhao F; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610000, China. Electronic address: zhaofeixiang@cdut.edu.cn.
  • Li D; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610000, China. Electronic address: lidongfen17@cdut.edu.cn.
  • Luo R; Department of Nuclear Medicine, Mianyang Central Hospital, Mianyang, 621000, China. Electronic address: luo919424962@gmail.com.
  • Liu M; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610000, China. Electronic address: liumz@cdut.edu.cn.
  • Jiang X; School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325000, China. Electronic address: jiangxin@cqut.edu.cn.
  • Hu J; Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, China. Electronic address: hujunjie@scu.edu.cn.
Comput Biol Med ; 165: 107391, 2023 10.
Article in En | MEDLINE | ID: mdl-37717529
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Full text: 1 Database: MEDLINE Main subject: Positron Emission Tomography Computed Tomography / Deep Learning Type of study: Clinical_trials Language: En Year: 2023 Type: Article
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Full text: 1 Database: MEDLINE Main subject: Positron Emission Tomography Computed Tomography / Deep Learning Type of study: Clinical_trials Language: En Year: 2023 Type: Article