Author ORCID Identifier

https://orcid.org/0000-0003-1209-4406

Date of Award

Spring 2024

Document Type

Thesis (Ph.D.)

Department or Program

Engineering Sciences

First Advisor

Ryan J. Halter

Second Advisor

Joseph A. Paydarfar

Abstract

Transoral robotic surgery (TORS) is a minimally invasive approach in treating head and neck cancers and has demonstrated improved surgical outcomes with reduced morbidity when compared to traditional open surgery. However, the lack of haptic feedback makes it more difficult to assess the tumor extent and locate critical structures (e.g., artery and nerves). In standard-of-care TORS, preoperative imaging is used as guidance; however, it becomes inaccurate intraoperatively: the patient’s neck is hyper-extended, surgical instruments are introduced, and soft tissues deform significantly. As a result, the surgeon is required to mentally predict intraoperative deformation, which can lead to bleeding complications and higher margin positivity.

We hypothesize that image-guided TORS (igTORS) can help compensate for the sensory deficit and improve surgical efficacy and safety. Current challenges in achieving igTORS include the lack of intraoperative imaging and a compatible navigation framework. Therefore, this work aims to (1) develop an imaging-compatible oral retractor system to enable intraoperative image guidance in TORS, (2) develop a surgical navigation framework for TORS that utilizes electromagnetic (EM) and optical tracking, and (3) demonstrate the potential efficacy of using igTORS compared to traditional TORS.

First, we developed a sterilizable polymer oral retractor system that enabled high-resolution and artifact-free intraoperative CT/MR images and demonstrated adequate surgical exposure, TORS-compatibility, and strength of the system. Then we developed a navigation framework that integrated real-time robotic instrument tracking and a 3D model synchronized with the stereoendoscopic view that could be displayed on the surgeon’s console via TilePro. Finally, we conducted phantom and cadaveric studies following igTORS and traditional TORS workflow and achieved a significant 88% reduction (p=0.0079) in target localization error (TLE) in a cadaveric study. Our findings strongly indicate that implementing igTORS can significantly improve surgical targeting accuracy in TORS. Future work will focus on conducting clinical trials of the retractor system and implementing image overlay in the navigation framework.

Original Citation

  • Shi Y, et al., “Development of an imaging-compatible oral retractor system for image-guided transoral robotic surgery,” Annals of Biomedical Engineering, accepted (2024)
  • Shi Y, et al., “A surgical navigation framework for image-guided transoral robotic surgery,” Proc, SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling (2024)
  • Shi Y, et al., “Surgical navigation system for image-guided transoral robotic surgery: a proof of concept,” Proc. SPIE 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling (2022)
  • Shi Y, et al., “Imaging-compatible oral retractor system for use in image-guided transoral robotic surgery,” Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling (2021)
  • Sramek M, Shi Y, et al., “Tumor phantom for training and research in transoral surgery,” Laryngoscope Investigative Otolaryngology 5(4), 677-682 (2020)

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