A Hybrid Neural Network Electromagnetic Inversion Scheme (HNNEMIS) for Super-Resolution 3-D Microwave Human Brain Imaging

Abstract

Super-resolution three-dimensional (3-D) electromagnetic (EM) inversion for microwave human brain imaging is a typical high contrast EM inverse problem and requires huge computational costs. This work proposes a hybrid neural network electromagnetic inversion scheme (HNNEMIS) which contains shallow and deep neural networks to alleviate the required huge computational costs and solve this high contrast inverse problem. In the proposed scheme, semi-join back propagation neural network (SJ-BPNN) is employed to nonlinearly map the measured scattered electric field to two output channels, namely the permittivity and conductivity of scatterers, respectively. Such a semi-join strategy decreases the computational burden in training and testing processes. Then, a deep learning technique, termed U-Net, is employed to further enhance the imaging quality of the output from SJ-BPNN. To decrease the training cost and make neural networks fast convergent for human brain inversion, a novel training dataset construction strategy which contains the characteristics of human brain is also proposed. Noise-free and noisy numerical examples demonstrate that HNNEMIS has superior super-resolution inversion capabilities for human brain imaging.