Design, Modeling, and Control of Minimally Invasive Robotic Surgical Systems with Integrated Sensors
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Deaton, Nancy Joanna
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Abstract
Manual manipulation of passive surgical tools can be challenging and may provide limited access to target locations deep within the body. For example, in brachytherapy cancer treatment, hollow needles must be delivered to a target area for radiation, such as the prostate, which is located around the urethra and must not be damaged. The accuracy of needle placement affects both the ability to achieve the pre-planned radiation dose distribution and to minimize damage to nearby healthy anatomical structures. This dissertation presents the design of a robotically steerable needle system capable of navigating along a desired curved path achieved by using a tendon-driven robotic joint. First, the assembly of micro-scale and meso-scale robotic joints is outlined, and different attachment methods for both nitinol and tungsten tendons are studied. The steerable needle system consists of a steerable stylet made from a micromachined superelastic nitinol tube to create a tendon-driven bending joint. By offsetting the placement of tendons from the neutral axis of the joint, the system can be bent in multiple directions. Finite element modeling is used to determine the parameters for the micromachined joint, and a model is derived to estimate the deflection due to the tendon-pulling force. Control is implemented using electromagnetic (EM) tracking, and the steerable needle is shown to effectively navigate along a desired curved path through a hydrogel tissue phantom. However, EM tracking may not be practical in an operating room due to interference from other devices. Therefore, this work investigates the use of intrinsic fiber Bragg grating (FBG) sensors. A planar FBG bending sensor is created and shown to be capable of measuring curvatures as large as 145 m−1 . This sensor is effectively implemented for state estimation of the bending angle in both meso-scale and micro-scale surgical robotic devices. To expand this work, a three-dimensional (3D) FBG-based shape sensor is created first using an FBG fiber in series with a superelastic nitinol spring. However, this sensor could not be miniaturized, so an FBG triplet is then created to achieve a miniaturized 3D shape sensor and characterized under different use cases by varying the rate and duration of deflection as well as the surrounding temperature. The sensor is found to provide a reliable response with some relatively small drift when deflected for long periods of time. This 3D FBG sensor is then implemented in the micro-scale COaxially Aligned STeerable (COAST) guidewire robot, which is modified to facilitate the integration of the sensor and to create a force-sensing tip at the distal end. The COAST guidewire is capable of follow-the-leader (FTL) motion for navigating tortuous vasculature, which is currently a challenge in cardiovascular interventions. Current visualization requires X-ray imaging, which should be minimized, so intrinsic shape feedback is implemented in this work to improve future control of this robot. Multiple shape reconstruction approaches to model the COAST guidewire using the FBG feedback are derived and compared. Furthermore, the most distal FBG sensing segment is isolated in the tip and correlated to external forces to provide feedback toward safe interaction with surrounding structures. The design of this guidewire is then adapted to create a steerable robot capable of FTL motion to facilitate the navigation of delicate tissues within the brain. The steerable robot delivers a hollow sheath capable of stiffening in a curved configuration to facilitate the passage of clinical devices such as a stereoelectroencephalography (SEEG) depth electrode used to diagnose epilepsy. Building towards the ultimate goal of this research to safely navigate complex anatomies and deliver minimally invasive procedures to target locations that are currently challenging to access, the work presented in this dissertation demonstrates my contributions towards the design, modeling, and control of minimally invasive robotic surgical systems with integrated sensors.
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2024-08-15
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Dissertation (PhD)