CNN for Compressibility to Permittivity Mapping for Combined Ultrasound-Microwave Breast Imaging

Abstract

Combined ultrasound-microwave breast imaging requires a mechanism to guide one imaging modality using the other. To this end, a convolutional neural network (CNN) is proposed for the mapping of ultrasound property images to dielectric property images for combined ultrasound-microwave breast imaging applications. In this approach, higher resolution ultrasound images are obtained by inverting the ultrasound scattered pressure data using a linearized inverse scattering algorithm. The reconstructed Born-based ultrasound compressibility images are then used to predict dielectric images for the same breast phantoms. These predicted dielectric images can then be used to guide microwave imaging reconstruction to achieve higher accuracy images. To this end, a CNN is trained based on the input of the reconstructed quantitative ultrasonic compressibility and the output of the true quantitative dielectric properties corresponding to the same numerical phantom. Several numerical MRI-derived breast phantoms are used to train and test this CNN. The predicted dielectric properties are tested using different numerical MRI-derived breast phantoms and the predicted profiles show promising results. The predicted dielectric properties are also used as the initial guess prior for the microwave inversion algorithm which leads to the enhancement of the reconstruction of dielectric properties as compared to the blind microwave inversion.