AirNet: Fused analytical and iterative reconstruction with deep neural network regularization for sparse-data CT.

Journal: Medical physics
Published Date: Apr 30, 2020

Abstract

PURPOSE: Sparse-data computed tomography (CT) frequently occurs, such as breast tomosynthesis, C-arm CT, on-board four-dimensional cone-beam CT (4D CBCT), and industrial CT. However, sparse-data image reconstruction remains challenging due to highly undersampled data. This work develops a data-driven image reconstruction method for sparse-data CT using deep neural networks (DNN).

Authors

  • Gaoyu Chen
    Department of Nuclear Medicine, Rui Jin Hospital, School of Medcine, Shanghai Jiao Tong University, Shanghai, 200240, China.
  • Xiang Hong
  • Qiaoqiao Ding
    Department of Mathematics, National University of Singapore, 119077, Singapore.
  • Yi Zhang
    Department of Thyroid Surgery, China-Japan Union Hospital of Jilin University, Jilin University, Changchun, China.
  • Hu Chen
  • Shujun Fu
    School of Mathematics, Shandong University, Jinan, Shandong, 250100, China.
  • Yunsong Zhao
    Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA, 30322, USA.
  • Xiaoqun Zhang
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
  • Hui Ji
    Department of Mathematics, National University of Singapore, Singapore.
  • Ge Wang
    Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA.
  • Qiu Huang
    Department of Nuclear Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Hao Gao
    Institute of Pharmaceutical Analysis , College of Pharmacy , Jinan University , Guangzhou , Guangdong 510632 , China . Email: haibo.zhou@jnu.edu.cn ; Email: jzjjackson@hotmail.com ; Email: tghao@jnu.edu.cn.

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