Estimating the Effective Permittivity for Reconstructing Accurate Microwave-Radar Images.
We present preliminary results from a method for estimating the optimal effective permittivity for reconstructing microwave-radar images. Using knowledge of how microwave-radar images are formed, we identify characteristics that are typical of good images, and define a fitness function to measure the relative image quality. We build a polynomial interpolant of the fitness function in order to identify the most likely permittivity values of the tissue. To make the estimation process more efficient, the polynomial interpolant is constructed using a locally and dimensionally adaptive sampling method that is a novel combination of stochastic collocation and polynomial chaos. Examples, using a series of simulated, experimental and patient data collected using the Tissue Sensing Adaptive Radar system, which is under development at the University of Calgary, are presented. These examples show how, using our method, accurate images can be reconstructed starting with only a broad estimate of the permittivity range.
Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, T2N 1N4, Canada.
Benjamin R
BR
Lavoie
Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, T2N 1N4, Canada.
Michal
M
Okoniewski
Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, T2N 1N4, Canada.
Elise C
EC
Fear
D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D003198:Computer Simulation; D003952:Diagnostic Imaging / Q000379:methods / Q000592:standards; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D042361:Mammary Glands, Human / Q000000981:diagnostic imaging; D008872:Microwaves; D008962:Models, Theoretical; D011825:Radar