Dissertation
Fracture-directed Steerable Needles
Doctor of Philosophy (PhD), Washington State University
01/2020
Handle:
https://hdl.handle.net/2376/118454
Abstract
Steerable needles have been widely researched for many years. Since they have the availability to steer to a target point avoiding obstacles and correcting themselves for disturbances, they have great potential to improve the accuracy of both therapies and biopsies. However, the ability to make late-insertion corrections was limited by the attainable insertion radius of curvature.
This dissertation describes the design and modeling of a new class of steerable needles, the insertion process is to first control the direction of the tissue fracture with an inner nitinol wire and then follow with a hollow nitinol tube. This insertion approach has the capability to achieve a 6.9 mm insertion radius of curvature inside soft tissue phantoms with only 128Kpa Young's Modulus, and the radius of curvature is controllable from the lower limit up to a near-infinite insertion radius of curvature based on the tissue properties and needle step length.
A comprehensive predictive model was developed based on experimental data to predict the insertion radius of curvature across a wide range of tissue stiffnesses and a complete finite element analysis model was conducted to validate it. A variety of inner stylet geometries are investigated using tissue phantoms with multiple stiffnesses, and discrete-step kinematic models of motion are derived heuristically from the experiments.
A RG-RRT path planning algorithm and a straight-curve-straight heuristic path planning algorithm were developed to steer the needle in 2D or 3D space with obstacles. Both of them have the capability to conduct closed-loop re-planning based on real-time visual feedback.
This steerable needles research was motivated by reducing insertion radius, improving insertion accuracy, and ameliorate the clinical outcome.
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Details
- Title
- Fracture-directed Steerable Needles
- Creators
- Fan Yang
- Contributors
- John P Swensen (Advisor)Arda Gozen (Committee Member)Kuen-Ren Chen (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Mechanical and Materials Engineering, School of
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Number of pages
- 140
- Identifiers
- 99900581497801842
- Language
- English
- Resource Type
- Dissertation