Learnt Microwave Image Reconstruction with A Conformal Antenna Array

Abstract

A deep learning model is proposed for reconstructing 2D dielectric breast images from time-domain signals. Unlike existing learning models that employ a fixed antenna array, where input data consists solely of measurements, the proposed system integrates antenna positioning into the processing pipeline. This allows for a conformal antenna array that adapts to different breast sizes for optimal data collection across various patients, which eliminates undesired signal attenuation in coupling liquid when implemented for the fixed array. By leveraging antenna positions, the breast surface can be pre-estimated, enabling the neural network to focus on image reconstruction within the region of interest. Numerical results demonstrate that the proposed model may reconstruct breast images with good quality.