TY - GEN
T1 - Digital Twin Model Based Robot-Assisted Needle Insertion Navigation System with Visual and Force Feedback
AU - Du, Shilun
AU - Wang, Zhen
AU - Li, Murong
AU - Hu, Yingda
AU - Shen, Mengruo
AU - Xu, Tian
AU - Lei, Yong
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
PY - 2023
Y1 - 2023
N2 - In needle insertion navigation, most researches focus on intraoperative images based navigation system that provides only visual feedback. Besides, few navigation systems are integrated with insertion robot. In this paper, we proposed a digital twin model based robot-assisted needle insertion navigation system with visual and force feedback. Our system can predict needle deflection, tissue deformation for visual feedback and interaction force for force feedback while insertion robot can help steering needle for accurate insertion. The proposed needle insertion navigation system integrates digital twin model and insertion-assisted robot. A digital twin model of target organ, which includes finite element model and visual model, can be generated based on preoperative CT image to predict needle deflection, tissue deformation and interaction force of planned needle path. Optic-based calibration method for our system is developed. A hybrid spring mapping method based on radial-basis function interpolation and spring-mass model is proposed as well for better visual feedback. The proposed navigation system can provide both visual feedback and force feedback in digital twin model for surgeons while robot can help steering needle to target position. Simulations and experiments are carried out for our navigation system and hybrid spring mapping method. Results show the calibrated system is accurate with 4mm targeting accuracy, which meets clinical accuracy requirements. Hybrid spring mapping method can update the visual model smoothly. Both force and visual feedback can be registered to the digital twin coordinate system, allowing for accurate and consistent feedback for navigation.
AB - In needle insertion navigation, most researches focus on intraoperative images based navigation system that provides only visual feedback. Besides, few navigation systems are integrated with insertion robot. In this paper, we proposed a digital twin model based robot-assisted needle insertion navigation system with visual and force feedback. Our system can predict needle deflection, tissue deformation for visual feedback and interaction force for force feedback while insertion robot can help steering needle for accurate insertion. The proposed needle insertion navigation system integrates digital twin model and insertion-assisted robot. A digital twin model of target organ, which includes finite element model and visual model, can be generated based on preoperative CT image to predict needle deflection, tissue deformation and interaction force of planned needle path. Optic-based calibration method for our system is developed. A hybrid spring mapping method based on radial-basis function interpolation and spring-mass model is proposed as well for better visual feedback. The proposed navigation system can provide both visual feedback and force feedback in digital twin model for surgeons while robot can help steering needle to target position. Simulations and experiments are carried out for our navigation system and hybrid spring mapping method. Results show the calibrated system is accurate with 4mm targeting accuracy, which meets clinical accuracy requirements. Hybrid spring mapping method can update the visual model smoothly. Both force and visual feedback can be registered to the digital twin coordinate system, allowing for accurate and consistent feedback for navigation.
KW - Digital twin model
KW - Needle insertion navigation system
KW - Needle-tissue interaction model
KW - Robot-assisted insertion
UR - https://www.scopus.com/pages/publications/85175994039
U2 - 10.1007/978-981-99-6489-5_10
DO - 10.1007/978-981-99-6489-5_10
M3 - 会议稿件
AN - SCOPUS:85175994039
SN - 9789819964888
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 117
EP - 131
BT - Intelligent Robotics and Applications - 16th International Conference, ICIRA 2023, Proceedings
A2 - Yang, Huayong
A2 - Zou, Jun
A2 - Yang, Geng
A2 - Ouyang, Xiaoping
A2 - Liu, Honghai
A2 - Wang, Zhiyong
A2 - Yin, Zhouping
A2 - Liu, Lianqing
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Conference on Intelligent Robotics and Applications, ICIRA 2023
Y2 - 5 July 2023 through 7 July 2023
ER -