Analysis of Incident Field Modeling and Incident/Scattered Field Calibration Techniques in Microwave Tomography

Imaging with microwave tomography systems requires both the incident field within the imaging domain as well as calibration factors that convert the collected data to corresponding data in the numerical model used for inversion. The numerical model makes various simplifying assumptions, e.g., 2-D versus 3-D wave propagation, which the calibration coefficients are meant to take into account. For an air-based microwave tomography system, we study two types of calibration techniques-incident and scattered field calibration-combined with two different incident field models: a 2-D line-source and an incident field from full-wave 3-D simulation of the tomography system. Although the 2-D line-source approximation does not accurately model incident field in our system, the use of scattered field calibration with the 2-D line-source provides similar or better images to incident and scattered field calibration with an accurate incident field. Thus, if scattered field calibration is used, a simple (but inaccurate) incident field is acceptable for our microwave tomography system. While not strictly generalizable, we expect our methodology to be applicable to most other microwave tomography systems.

I. Introduction

I n microwave tomography (MWT), a quantitative map, or image, of the dielectric properties of an object of interest(OI) is obtained from a limited set of electromagnetic field measurements made outside the OI. An inverse scattering algorithm is then utilized to reconstruct the image from the measurement data [1]. Inversion algorithms: 1) require the input of an incident field inside the imaging domain; and 2) require calibration of experimental data. This is because the algorithms assume an idealized electromagnetic model of the physical system that simplifies, or ignores, the antennas (field-probes), the finite extent of the imaging chamber, as well as cables leading from the transmitter/receiver instrumentation to the antennas and often makes a 2-D assumption about 3-D wave propagation. The process of calibration may be viewed as an attempt to convert collected experimental data to the assumed numerical model. In addition, though only circuit quantities can be directly measured, most inversion algorithms require field values at appropriate spatial locations/regions within their assumed electromagnetic model. For example, both the Gauss–Newton Inversion (GNI) [2] and the Contrast Source Inversion (CSI) techniques [3] require scattered field quantities at several measurement points surrounding the OI as well as an accurate characterization of the incident field inside the imaging domain.