Estimation and Use of Prior Information in FEM-CSI for Biomedical Microwave Tomography

Prior information is used to improve imaging results obtained using the finite-element contrast source inversion ( FEM-CSI ) of a microwave tomography (MWT) dataset collected as part of a forearm imaging study. The data consist of field measurements taken inside a prototype MWT system that uses simple dipole antennas and a saltwater matching medium. Initial images of the 2-D cross-sectional dielectric profile of the individuals' arms are reconstructed using FEM-CSI. These initial “blind” imaging results show that the image quality is dependent on the thickness of the arm's peripheral adipose tissue layer: Thicker layers of adipose tissue lead to poorer overall image quality. The poor image quality for arms with high levels of adipose tissue is not improved by changing the matching fluid's complex dielectric constant. Introducing prior information into the FEM-CSI algorithm in the form of an inhomogeneous background consisting of an adipose layer surrounding a muscle region provides substantial improvement of the image quality: The internal anatomical features of the arm are resolved for each of the five datasets. Two methods are employed to estimate the arm periphery and adipose layer thickness from the blind imaging results: manual estimation and a novel image segmentation algorithm based on global optimization using simulated annealing.

I. Introduction

Microwave tomography (MWT) is a modality that has shown potential in various biomedical imaging applications such as breast cancer detection and monitoring [1], as well as in extremity imaging [2]. Several challenges confront the quantitative inverse scattering algorithms associated with the fully nonlinear biomedical imaging problem. One of the biggest challenges is that most biomedical objects of interest (OIs) are highly inhomogeneous with respect to the complex relative permittivity profile that is to be reconstructed. The range of permittivity values corresponding to human tissues is large, and sometimes two tissues of interest differ by only a few percent [3]. Adjacent tissues of widely differing permittivity may also shadow each other with respect to the interrogating microwave energy. This is the case, for example, when an adipose layer surrounds muscle tissue, each having a complex relative permittivity of and , respectively, at 1 GHz. Add to the possible shadowing effects the large amount of multiple scattering inherent in such highly inhomogeneous regions, and it becomes clear why the nonlinear inverse scattering problem is so difficult to solve.