Date of Award

8-2014

Document Type

Thesis

Degree Name

Master of Science (MS)

Legacy Department

Bioengineering

Advisor

Kwartowitz, David M

Committee Member

Gao , Bruce

Committee Member

Dean , Delphine

Abstract

Image guided surgery (IGS) is an integral part of minimally invasive surgery. IGS combines pre- and perioperative images acquired from different imaging modalities to give the surgeon a more complete view of the internal organs. These modalities include computed tomography, magnetic resonance imaging, and fluoroscopy, to name a few. Fluoroscopy is also known as video x-ray and is becoming increasingly popular in procedures around the heart. Unfortunately, this increase in fluoroscopy use also brings an increase in exposure to ionizing radiation for the patient and the surgeon. This radiation can lead to increased cancer risk and a number of other problems. Studies show that medical radiation exposure has increased six times from 1992 to 2009. This exposure accounts for approximately half of all radiation exposure that humans receive with background radiation being the only source larger. Of the medical exposure, fluoroscopy accounts for approximately 25%. An increasingly popular trend in IGS is the use of predictive modeling. Davatzikos, et al, presents a framework for predictive modeling of anatomical structures but focuses on simple structures like ovals and circles. We seek to apply this framework to a more complex organ with more complex motions such as the heart. A predictive model of the heart could provide the surgeon with an effective partial replacement to fluoroscopy. This could significantly reduce radiation exposure as well as the risks of associated diseases.

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