Date of Graduation
12-2021
Document Type
Thesis
Degree Name
Master of Science in Mechanical Engineering (MSME)
Degree Level
Graduate
Department
Mechanical Engineering
Advisor/Mentor
Chen, Yue
Committee Member
Wejinya, Uche C.
Second Committee Member
Hu, Han
Keywords
Active Needle Insertion; Image-guided Therapy; Medical Robotics
Abstract
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death in the world. Radiofrequency ablation (RFA) is an effective method for treating tumors less than 5 cm. However, manually placing the RFA needle at the site of the tumor is challenging due to the complicated respiratory induced motion of the liver. This paper presents the design, fabrication, and benchtop characterization of a patient mounted, respiratory compensated robotic needle insertion platform to perform percutaneous needle interventions. The robotic platform consists of a 4-DoF dual-stage cartesian platform used to control the pose of a 1-DoF needle insertion module. The active needle insertion module consists of a 3D printed flexible fluidic actuator capable of providing a step-like, grasp-insert-release actuation that mimics the manual insertion procedure. Force characterization of the needle insertion module indicates that the device is capable of producing 22.6 ± 0.40 N before the needle slips between the grippers. Static phantom targeting experiments indicate a positional error of 1.14 ± 0.30 mm and orientational error of 0.99° ± 0.36°. Static ex-vivo porcine liver targeting experiments indicate a positional error of 1.22 ± 0.31 mm and orientational error of 1.16° ± 0.44°. Dynamic targeting experiments with the proposed active motion compensation in dynamic phantom and ex-vivo porcine liver show 66.3% and 69.6% positional accuracy improvement, respectively. Future work will continue to develop this platform with the long-term goal of applying the system to RFA for HCC.
Citation
Musa, M. J. (2021). Respiratory Compensated Robot for Liver Cancer Treatment: Design, Fabrication, and Benchtop Characterization. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/4280
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Artificial Intelligence and Robotics Commons, Biomechanical Engineering Commons, Computer-Aided Engineering and Design Commons, Robotics Commons