Multi-material decomposition of spectral CT images via Fully Convolutional DenseNets.

Journal: Journal of X-ray science and technology
Published Date: Jan 1, 2019

Abstract

BACKGROUND: Spectral computed tomography (CT) has the capability to resolve the energy levels of incident photons, which has the potential to distinguish different material compositions. Although material decomposition methods based on x-ray attenuation characteristics have good performance in dual-energy CT imaging, there are some limitations in terms of image contrast and noise levels.

Authors

  • Xiaochuan Wu
    The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
  • Peng He
    Key Laboratory of Sensor Analysis of Tumor Marker, Ministry of Education, College of Chemistry and Molecular Engineering, Qingdao University of Science and Technology, Qingdao 266042, PR China.
  • Zourong Long
    The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
  • Xiaodong Guo
    The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
  • Mianyi Chen
    The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
  • Xuezhi Ren
    The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
  • Peijun Chen
    The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
  • Luzhen Deng
    The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
  • Kang An
    ICT NDT Engineering Research Center, Ministry of Education, Chongqing University, Chongqing, China.
  • Pengcheng Li
    Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China. Electronic address: pcli@qdio.ac.cn.
  • Biao Wei
    The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
  • Peng Feng
    The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.

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