Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study.

Abstract

Authors

• Jingyu Zhong
  Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai, 200336, China.
• Yihan Xia
  Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
• Yong Chen
  Department of Urology, Chongqing University Fuling Hospital, Chongqing, China.
• Jianying Li
  CT Research Center, GE Healthcare China, Beijing 100176, China.
• Wei Lu
  Department of Pharmacy, Taihe Hospital, Hubei University of Medicine, Shiyan, China.
• Xiaomeng Shi
  From the Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, 100 Haining Rd, Shanghai 200080, China (B.J., N.L., X.X.); CT Imaging Research Center, GE Healthcare China, Shanghai, China (X.S., S.Z., J.L.); and Departments of Epidemiology (G.H.d.B.) and Radiology (R.V.), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
• Jianxing Feng
  Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai, China.
• Fuhua Yan
  Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin Er Road, Shanghai 200025, China. Electronic address: yfh11655@rjh.com.cn.
• Weiwu Yao
  Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai, 200336, China. yaoweiwuhuan@163.com.
• Huan Zhang
  Department of Plant Protection, Zhejiang University, 866 Yuhangtang Road, 5 Hangzhou 310058, China.

Keywords

External Resources