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Title page for ETD etd-01242017-111110


Type of Document Dissertation
Author Dillon, Neal Patrick
URN etd-01242017-111110
Title Analysis, Design, and Modeling of Image-Guided Robotic Systems for Otologic Surgery
Degree PhD
Department Mechanical Engineering
Advisory Committee
Advisor Name Title
Robert J. Webster III Committee Chair
Michael I. Miga Committee Member
Nabil Simaan Committee Member
Robert F. Labadie Committee Member
Thomas J. Withrow Committee Member
Keywords
  • Image-guided surgery
  • medical robotics
  • cochlear implantation
  • mastoidectomy
  • robotic bone milling
  • robot trajectory planning
Date of Defense 2016-12-12
Availability restrictone
Abstract
Otology and neurotology are surgical specialties focusing on the treatment of ear diseases. A key component of many otologic and neurotologic surgical procedures is the

removal of a portion of the skull behind the ear to gain access to subsurface anatomy.

This process, called a mastoidectomy, is performed manually with a high speed surgical drill. Many vital structures, including nerves and blood vessels, are embedded

within the temporal bone near the region of bone that must be removed, which makes

the procedure difficult, time consuming, and in some cases, overly invasive.

Image-guided and robotic systems have the potential to improve otologic procedures using medical imaging to guide their interventions, enabling patient-specific treatments that reduce invasiveness and save valuable operating room time. However, since damage to the complex vital structures within the surgical field could result in severe consequences to the patient, any image-guided or robotic surgical system must be extremely safe and accurate. These requirements, along with the small surgical workspace and difficulty integrating systems into the current clinical workflow, have limited the adoption of such systems in otologic surgery to date.

This dissertation presents the design, experimentation, and analyses of image-guided, robotic systems under development for otologic surgery in an effort to bring these systems closer to clinical realization. The specific goals of the work are to better understand the technical requirements of various otologic surgical procedures, to improve the safety and efficiency of image-guided and robotic surgery by incorporating system modeling and medical image data into the surgical planning process, and to show feasibility and provide insights into practical issues through experimentation.

Two image-guided otologic procedures are explored in this work: (1) robotic mastoidectomy and (2) minimally invasive cochlear implantation. The technical requirements of robotic mastoidectomy are first explored to determine the necessary robot workspace and the required milling forces. Using these design requirements, a bone-attached robotic system is developed and tested in temporal bone specimens and fresh human cadaver heads. Next, planning algorithms to improve the safety and efficiency of robotic mastoidectomy are described. A method for building patient-specific safety margins around vital anatomy based on probabilistic error models of the robotic system, required safety rates, and simulations of the surgery is provided. A second planning algorithm is presented, which improves robot trajectory generation for milling porous bone in close proximity to vital anatomy by using CT image-based force modeling to optimize tool orientation and velocity.

The focus then shifts to minimally invasive, image-guided cochlear implantation. Two key safety issues are investigated: the positional accuracy of drilling a narrow tunnel towards the cochlea for electrode insertion and the heat rise near vital nerves during drilling. A method for pre-operative, patient-specific risk assessment utilizing the CT scan, modeling of the bone drilling process, and anatomical conditions is presented, followed by an improved surgical drilling approach. Finally, an experimental setup enabling direct temperature measurement of the bone near the facial nerve in cadavers is developed and used to validate the modeling and surgical approach.

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