Improved Tumor Detection via Quantitative Microwave Breast Imaging Using Eigenfunction-Based Prior

Abstract

A multistage algorithm for quantitative microwave breast imaging is presented which utilizes the eigenfunction-based reconstruction of the complex-valued permittivity as prior information. The eigenfunction-based reconstruction is obtained from a single-frequency non-iterative microwave inversion technique that uses the eigenfunctions of the Helmholtz operator, in a resonant conductive enclosure, as the expansion basis. The low-resolution eigenfunction-based reconstruction is incorporated into the Contrast Source Inversion technique as an inhomogeneous numerical background. The use of this prior information improves the stability of the inversion algorithm, and results in better detectability of tumors. The multistage algorithm's performance is demonstrated by applying it to synthetic data obtained from three 2D MRI-derived anthropomorphic breast models with various densities, and shapes. The algorithm's efficacy in tumor detection is assessed by investigating detection results using prior information obtained with the number of eigenfunction in the expansion basis truncated with three different values. Numerical experiments are performed using four different frequencies. The main advantage of obtaining prior information using this method, as opposed e.g. in using radar or ultrasound derived prior, is that it utilizes the same microwave set-up, and only microwave interrogating fields.