Microwave imaging is a branch of electromagnetic applications. The relatively long wavelengths of microwaves (from a millimeter to a meter) allow for penetration into many optically opaque mediums, such as living tissues. Microwave imaging is a non-ionizing and potentially low cost imaging modality with the aim of distinguishing between healthy and malignant tissues. Similar to other imaging modalities, it aids in the screening and detection of disease. Microwave imaging allows the internal structure of an object via the dielectric values to be seen by means of electromagnetic fields at microwave frequencies.
There are two primary approaches of microwave imaging: 1) pulsed radar and 2) tomography. The pulsed radar techniques seek to identify the presence and location of targets by their scattering signatures by analyzing the portion of the energy reflected back to the radar station. The most prominent pulsed-radar reconstruction methodology is confocal imaging which uses the principles of radar synthetic focusing.
In tomography, a microwave signal is transmitted by one antenna. Typically numerous antennas surround the object being imaged and receive the transmitted and/or reflected waves. However, this requires a large number of transmitting and receiving antennas surrounding the object of interest. This also poses a non-linear inverse scattering problem which is complex and very computationally intensive to solve. Excitation measurements are compared with calculations produced from numerical models to arrive at an update for the dielectric values. Different deterministic and stochastic algorithms serves as a basis for the non-linear reconstruction techniques. Among the stochastic approaches, evolutionary algorithms, offer a number of advantages including parallelism and no requirement for a differentiable objective function.