Semi-autonomous Magnetic Manipulation for Endovascular Navigation

Endovascular navigation is one of the significant barriers to stroke treatment. Due to its geometrical complexity, navigation inside the cerebral arteries becomes challenging as the destination moves further toward the distal region of the brain. Magnetic manipulation is one solution that can resolve this difficulty. By utilizing magnetic force and torque, magnetic manipulation enables active steering of the distal tip of the guidewire, thereby enhancing navigation capabilities. As a platform for magnetic manipulation, a single robot arm equipped with a permanent magnet has advantages over other magnetic field generators in terms of their footprint and cost. However, the manipulation of the permanent magnet attached to the end of the robot arm is not a trivial task. During the operation, a neurosurgeon has to consider the relative position and orientation of the end effector with respect to the current position of the guidewire. Moreover, the user must avoid collision between the robot and the patient in order to secure the patient’s safety. Without any assistance, these considerations leave the cognitive burden on the user’s side. In order to solve this problem, this thesis presents a semi-autonomous magnetic manipulation system for the single robot arm platform. In the system, an appropriate position and orientation of the magnet are obtained by aligning the desired field direction with the center axis of the magnet. For the calculated position and orientation, a corresponding robot configuration is obtained by solving the inverse kinematics problem of the robot arm. The 3 step motion planner is applied to find a valid robot trajectory to the optimal robot configuration while securing the patient’s safety. The system is integrated into an interface, and the performance of the semi-autonomous system is verified through an experiment on an artificial cerebral artery model.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Choe, Jaehun

Advisor dc:contributor.advisor

• Zhao, Xuanhe

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/150060

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/150060