TY - GEN
T1 - Fully Robotized 3D Ultrasound Image Acquisition for Artery
AU - Chen, Mingcong
AU - Huang, Yuanrui
AU - Chen, Jian
AU - Zhou, Tongxi
AU - Chen, Jiuan
AU - Liu, Hongbin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Current imaging of the artery relies primarily on computed tomography angiography (CTA), which requires contrast injections and exposure to radiation. In this paper, we present a method for fully autonomous artery 3D image acquisition using a linear ultrasound (US) probe and a 6 DoFs robot arm with a 3D camera. Robotic vessel acquisition can minimize tissue deformation and permit the reproduction of scans. Additionally, the robotic-based acquisition can provide more precise vessel position data that can be utilized for 3D reconstruction as a preoperative image. The first scanning point is determined by the 3D camera using a neural network for leg area estimation. A visual servo algorithm adjusts the in-plane motions using a cross-sectional vessel segmentation produced by a neural network with a UNet structure, while a US confidence map regulates the in-plane rotation. The robot is equipped with impedance control to maintain a constant and safe scan. Experiments on a leg phantom and a volunteer indicate that the robot can follow the vessel and modify its position to provide a sharper US image. The average error of phantom scanning in y-axis and z-axis are 0.2536mm and 0.2928mm, respectively, while the root means square error (RMSE) of contact force in the volunteer experiment is 0.2664N. In addition, a 3D vessel reconstruction demonstrates the possibility of robotic US acquisition as a preoperative image.
AB - Current imaging of the artery relies primarily on computed tomography angiography (CTA), which requires contrast injections and exposure to radiation. In this paper, we present a method for fully autonomous artery 3D image acquisition using a linear ultrasound (US) probe and a 6 DoFs robot arm with a 3D camera. Robotic vessel acquisition can minimize tissue deformation and permit the reproduction of scans. Additionally, the robotic-based acquisition can provide more precise vessel position data that can be utilized for 3D reconstruction as a preoperative image. The first scanning point is determined by the 3D camera using a neural network for leg area estimation. A visual servo algorithm adjusts the in-plane motions using a cross-sectional vessel segmentation produced by a neural network with a UNet structure, while a US confidence map regulates the in-plane rotation. The robot is equipped with impedance control to maintain a constant and safe scan. Experiments on a leg phantom and a volunteer indicate that the robot can follow the vessel and modify its position to provide a sharper US image. The average error of phantom scanning in y-axis and z-axis are 0.2536mm and 0.2928mm, respectively, while the root means square error (RMSE) of contact force in the volunteer experiment is 0.2664N. In addition, a 3D vessel reconstruction demonstrates the possibility of robotic US acquisition as a preoperative image.
UR - http://www.scopus.com/inward/record.url?scp=85168695772&partnerID=8YFLogxK
U2 - 10.1109/ICRA48891.2023.10161148
DO - 10.1109/ICRA48891.2023.10161148
M3 - Conference Proceeding
AN - SCOPUS:85168695772
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 2690
EP - 2696
BT - Proceedings - ICRA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Robotics and Automation, ICRA 2023
Y2 - 29 May 2023 through 2 June 2023
ER -