Fully Automatic Dual-Probe Lung Ultrasound Scanning Robot for Screening Triage.

Journal: IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Published Date:

Abstract

Two-dimensional lung ultrasound (LUS) has widely emerged as a rapid and noninvasive imaging tool for the detection and diagnosis of coronavirus disease 2019 (COVID-19). However, image differences will be magnified due to changes in ultrasound (US) imaging experience, such as US probe attitude control and force control, which will directly affect the diagnosis results. In addition, the risk of virus transmission between sonographer and patients is increased due to frequent physical contact. In this study, a fully automatic dual-probe US scanning robot for the acquisition of LUS images is proposed and developed. Furthermore, the trajectory was optimized based on the velocity look-ahead strategy, the stability of contact force of the system and the scanning efficiency were improved by 24.13% and 29.46%, respectively. Also, the control ability of the contact force of robotic automatic scanning was 34.14 times higher than that of traditional manual scanning, which significantly improves the smoothness of scanning. Importantly, there was no significant difference in image quality obtained by robotic automatic scanning and manual scanning. Furthermore, the scanning time for a single person is less than 4 min, which greatly improves the efficiency of screening triage of group COVID-19 diagnosis and suspected patients and reduces the risk of virus exposure and spread.

Authors

  • Jiyong Tan
    Department of Mechanical and Energy Engineering, SUSTECH-AISONO Joint Lab in Robotics, Southern University of Science and Technology, Shenzhen, China.
  • Bing Li
  • Yuquan Leng
  • Yuanwei Li
  • Junhua Peng
  • Jiayi Wu
    State Key Laboratory for Artificial Microstructure and Mesoscopic Physics, Institute of Condensed Matter Physics, School of Physics, Center for Quantitative Biology, Peking University, Beijing, China.
  • Baoming Luo
    Department of Ultrasound, The Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
  • Xinxing Chen
  • Yiming Rong
  • Chenglong Fu