Optimization-Based Confocal Microwave Imaging in Medical Applications

Abstract

An optimization-based confocal algorithm for microwave imaging aimed at medical applications is presented. Due to complexity of human body tissues, microwave signals that enter a human organ from different angles and are reflected at different depths inside that organ face different effective dielectric constants. An accurate estimation of those dielectric constants is vital for an accurate estimation of signal speed within the imaged object and thus accurate resultant images. The traditional confocal algorithm uses one preassumed effective dielectric constant and thus its resultant image is sensitive to that assumption. In the proposed approach, position-dependent dielectric constants are used to generate a highly focused image that accurately maps scatterers within an imaged object. To that end, an objective equation based on the focusing degree of the obtained initial image is created. Then, particle swarm optimization (PSO) uses that equation to find the effective dielectric constants facing signals penetrating the object according to their entrance point. Those optimized effective dielectric constants are used to produce a highly focused image without the risk of creating false-positive targets irrespective of the initial values of the effective dielectric constants. The proposed algorithm is evaluated in head imaging via full-wave electromagnetic simulations and experiments. The simulations are based on using an accurate numerical head model, whereas the experiments are conducted using realistic artificial head phantoms. The obtained images indicate that the quality of the images is significantly improved with more accurate localization of targets in unhealthy cases and less probability of positive false alarms in healthy cases compared with the traditional approach.