Abstract

There have been many important contributions to imaging for biomedical applications. The most popular methods include X-ray mammography, magnetic resonance imaging (MRI), ultrasound, and most recently, microwave imaging. While the first three of these have been used for biomedical applications for over three decades, microwave imaging has seen many developments over the last few years. This is primarily due to the large contrast in electrical parameters between different body tissues (including differences between healthy and diseased tissues) at microwave frequencies. There are also vast improvements possible for the comfort of the patient undergoing such imaging as compared to mammography. However, there has been no relevant work to date on extraction of the electrical characteristics of tissues within a living patient. Rather, all of the work in the field of microwave imaging has focused on utilizing the vast contrast in electrical parameters to create an image of internal body structures. The electrical properties of human body tissues can be considered as non-magnetic, lossy, frequency-dependent dielectrics in the general case. All that is needed to fully describe these tissues is the frequency-dependent complex relative permittivity. The present work focuses on a unique application of Ultra-Wideband (UWB) radar to extract the frequency-dependent electrical properties of tissues modeled as multiple layers of dielectric regions. By applying an incident pulse to this series of dielectric regions, and by analyzing the reflected signals, the electrical characteristics can be extracted. The results can be expressed in terms of frequency-dependent relative permittivity and conductivity. This work focuses on the time-domain processing to determine the thickness of dielectric regions. Also, a calibration method is proposed to remove interference from the outer dielectric region. Finally, a generalized methodology is proposed to extract the electrical parameters of multiple dielectric regions in the frequency-domain. In all cases, excellent agreement is found between extracted and expected results.

Publication Date

2004

Document Type

Thesis

Student Type

Graduate

Degree Name

Electrical Engineering (MS)

Department, Program, or Center

Electrical Engineering (KGCOE)

Advisor

Jayanti Venkataraman

Advisor/Committee Member

Daniel Phillips

Advisor/Committee Member

Sohail Dianat

Campus

RIT – Main Campus

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