A new deep learning method for displacement tracking from ultrasound RF signals of vascular walls.

Journal: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
PMID: 33341465

Abstract

It is necessary to monitor the mechanical properties of arteries which directly related to cardiovascular diseases (CVDs) in the early stages. In this study, we proposed a new method based on deep learning (DL) to track the displacement of the vessel wall from the ultrasound radio-frequency (RF) signals, which is a key technique to achieve quantitative measurement of vascular biomechanics. In comparison with traditional method, both results on simulation and experimental carotid artery data demonstrated that the DL method has higher accuracy for motion tracking of artery walls. Hence, the DL method can be widely applied so that can predict the early pathology of cardiovascular system.

Authors

  • Chenhui Xiao
    Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, 518060, China.
  • Zhenzhou Li
    Department of Ultrasound, Department of Ultrasound, The Second People's Hospital of Shenzhen, The First Affiliated Hospital of Shenzhen University, Shenzhen, 518060, China.
  • Jianfeng Lu
    Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, 518060, China.
  • Jinyan Wang
    Key Laboratory of Sustainable and Development of Marine Fisheries, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, PR China.
  • Haoteng Zheng
    Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, 518060, China.
  • Zuyue Bi
    Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, 518060, China.
  • Mengyang Chen
    Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, 518060, China.
  • Rui Mao
  • Minhua Lu
    Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, 518060, China. Electronic address: luminhua@szu.edu.cn.

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