Low-dose CT reconstruction via L1 dictionary learning regularization using iteratively reweighted least-squares.

Journal: Biomedical engineering online
Published Date: Jun 18, 2016

Abstract

BACKGROUND: In order to reduce the radiation dose of CT (computed tomography), compressed sensing theory has been a hot topic since it provides the possibility of a high quality recovery from the sparse sampling data. Recently, the algorithm based on DL (dictionary learning) was developed to deal with the sparse CT reconstruction problem. However, the existing DL algorithm focuses on the minimization problem with the L2-norm regularization term, which leads to reconstruction quality deteriorating while the sampling rate declines further. Therefore, it is essential to improve the DL method to meet the demand of more dose reduction.

Authors

  • Cheng Zhang
    College of Forestry, Jiangxi Agricultural University, Nanchang, Jiangxi Province, China.
  • Tao Zhang
    Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, 40044, People's Republic of China.
  • Ming Li
    Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.
  • Chengtao Peng
    Department of Electronic Science and technology, University of Science and Technology of China, Hefei, 230061, China.
  • Zhaobang Liu
    Suzhou Institute of Biomedical Engineering and Technology of Chinese Academy of Sciences, Suzhou, 215163, China.
  • Jian Zheng
    Biospheric Assessment for Waste Disposal Team, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, 4-9-1 Anagawa, Inage, Chiba 263-8555, Japan; Fukushima Project Headquarters, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, 4-9-1 Anagawa, Inage, Chiba 263-8555, Japan. Electronic address: zheng.jian@qst.go.jp.

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