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"Tissue margin sensing surgical drill using electrical impedance"

During our lifetime, we are likely to lose one or more teeth. Loss of teeth leads to poor mastication, unhealthy oral conditions, and low self-esteem. Implanting an artificial replacement tooth is an effective treatment that is becoming increasingly common. During surgical drilling for these implant procedures, there is a high likelihood of nerve injuries and sinus perforation. Although mitigation methods including computer assisted surgery and surgical navigation exists, they are expensive to implement and do not provide real-time tissue feedback to the surgeon. By electrically insulating a standard drill-bit and leaving the drill-tip exposed, impedance measurements can be localized to characterize the tissue near the tip. While drilling through the jaw, changes in impedance measurements could be used to alert the proximity of nerve or sinus boundaries. Viability of this method has been demonstrated using bench-top saline and in-situ bone tissue experiments. We investigate and address important challenges for successfully translating this technology for clinical use.

First, we focus on optimizing the sensor geometry using computer simulations. A custom simulation platform was constructed to minimize mismatch to real world experiments, which included an adaptive mesh refinement strategy and an empirical contact impedance formula. This framework was used to optimize the drill-bit sensor geometry. The optimized device was evaluated in an in-vivo swine model using intra-operative imaging and instrument tracking to aid in validation. Post operation, we propose to use micro-CT to understand the microstructure of bones surrounding the drill site. Using this data, we will establish relation between in-vivo bone electrical impedance properties and bone structures. Finally, we propose to use this information to develop a real-time tissue interface sensing algorithm and test its performance using in-vivo animal studies. By achieving these targets, we hope to demonstrate this technology working in animals and form the basis for future human clinical trials.

Thesis Committee

• Prof. Ryan Halter ( Chair / Advisor)
• Prof. Ethan Murphy
• Prof. Kofi Odame
• Prof. Xiaoyao Fan
• Prof. Ørjan Grøttem Martinsen, Head of Electronics, Department of Physics, University of Oslo

Contact

For more information, contact Theresa Fuller at theresa.d.fuller@dartmouth.edu.