High-Precision Cone-Beam CT Guidance for Head and Neck Surgery

Nathaniel Hamming

Graduate Department of the Institute of Biomaterials and Biomedical Engineering
University of Toronto 2009

ABSTRACT
Modern image-guided surgery aids minimally-invasive, high-precision procedures that increase efficacy of treatment. This thesis investigates two research aims to improve precision and integration of intraoperative cone-beam CT (CBCT) imaging in guidance of head and neck (H&N) surgery. First, marker configurations were examined to identify arrangements that minimize target registration error (TRE). Best arrangements minimized the distance between the configuration centroid and surgical target while maximizing marker separation. Configurations of few markers could minimized TRE with more markers providing improved uniformity. Second, an algorithm for automatic registration of image and world reference frames was pursued to streamline integration of CBCT with real-time tracking and provide automatic updates per scan. Markers visible to the tracking and imaging systems are automatically co-localized and registered with equivalent accuracy and superior reproducibility compared to conventional registration. Such work helps the implementation of CBCT in H&N surgery to maximize surgical precision and exploit intraoperative image guidance.

ACADEMIC BIOGRAPHY

Nathaniel Hamming is the 2009 recipient of the ITHC Thesis award in the Clinical Biomedical Engineering Program in the Institute of Biomaterials and Biomedical Engineering at the University of Toronto. He began the program in September, 2007 after completing a Bachelor of Science in Engineering and a Bachelor of Computer Science at the University of New Brunswick. Nathaniel's courses focused on developing a broad foundation of knowledge needed to be successful within the Clinical Biomedical Engineering field and internships provided a well rounded experience in medical device development and medical research within a clinical environment.

Currently Nathaniel is employed at the University Health Network in Toronto and works with the Medical Device Informatics Group to develop a diabetes management tool for the iPhone / iPod Touch mobile platform. This work supports his desire to empower patients and assist the transition from traditional health centre-based therapy to home-base therapy.