High-Resolution Microwave Breast Imaging Using a 3-D Inverse Scattering Algorithm With a Variable-Strength Spatial Prior Constraint

Microwave inverse scattering is an exploratory imaging modality with potential for several clinical breast imaging applications, including density evaluation, cancer detection, and treatment monitoring. However, conventional regularization techniques used to solve the ill-posed inverse problem typically result in blurred boundaries between tissue structures exhibiting dielectric contrast, thereby limiting the effective resolution. We present a method to improve microwave breast imaging resolution that incorporates a priori information about the boundaries between different tissues in the breast into the inverse scattering algorithm. This spatial prior information can be derived from another imaging modality, such as magnetic resonance imaging. Our method exploits the fact that the dielectric properties within a tissue type exhibit low to moderate variability by favoring solutions to the inverse scattering problem, which have small variations in dielectric properties within each tissue region. The amount of variation tolerated in each regions is controlled by a spatial prior constraint parameter. We demonstrate the feasibility of the method by imaging detailed, anatomically inspired numerical 3-D breast phantoms. The performance in the presence of different levels of noise and for different choices of the constraint parameter is evaluated. We also demonstrate the robustness of the algorithm with respect to errors in the spatial prior information.