Sensors and System Integration for Magnetic Resonance Image (MRI)-Guided and Robot-Assisted Interventions

Demand for magnetic resonance image (MRI)-guided interventions and robot-assisted surgeries continues to increase in the field of medical robotics. MRI-guided interventions provide pre-/intra-operative MR images and intraoperative manipulations. Robot-assisted surgeries guarantee higher safety and greater dexterity inside the patient’s body. Integration of MRI-guided and robot-assisted intervention techniques bring better precision, increased dexterity, and improved 3D visualization for surgery planning and real-time MR guidance. Despite the significant benefits of these techniques, potential challenges still remain: (i) for localization and tracking of MR-compatible manipulators using MR-visible markers, (ii) for 3D visualization of catheters and blood vessels in planning and guidance of interventional devices, (iii) for complementary characterization of human tissues and organs using multimodality imaging approaches, (iv) for higher flexibility and accessibility to overcome lack of navigation and limited workspace inside the human body and the MRI scanner. This dissertation describes the development of several enabling technologies for the MRI-guided and robot-assisted interventions. The specific technologies encompassed are: (i) an inductively coupled radio frequency (ICRF) coils that are optically tuned and detuned by the control of an MR-compatible manipulator for accurate localization and fast tracking, (ii) a novel method for 3D reconstruction of tubular structures such as catheters and blood vessels from three orthogonal MR projection images, (iii) an MR-compatible optical encoder for position feedback of the MR-compatible manipulators, (iv) a manipulator-mounted magnetic resonance spectroscopy (MRS) / light induced fluorescence (LIF) probe for multimodality bioimaging and biosensing applications, (v) an MR-compatible dexterous robotic manipulator for providing higher flexibility and accessibility, (vi) and the integration of the above mentioned techniques for the MRI-guided and robot-assisted interventions.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Houston

Year dc:date.issued

2016

Author dc:creator

• An, Junmo

Advisor dc:contributor.advisor

• Tsekos, Nikolaos V.

Committee members dc:contributor.committeemember

• Leiss, Ernst L.
• Shi, Weidong
• Shah, Dipan J.
• Stafford, R. Jason

• MRI-Guided
• Robot-Assisted
• MRI Compatibility
• Biosensors
• MR-Compatible Manipulator

Statement dc:rights

• The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/10657/3273

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/3273