MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNN

Magnetic induction tomography (MIT) is an efficient solution for long-term brain disease monitoring. It focuses on reconstructing the brain's bio-impedance distribution through nonintrusive electromagnetic fields. However, high-quality reconstruction of brain images remains a significant challenge, as reconstructing images from weak and noisy signals is a highly nonlinear and ill-conditioned problem. In this work, we propose a generative adversarial network (GAN) enhanced MIT technique, named MITNet, based on a complex convolutional neural network (CNN). MITNet takes complex-valued signals as input and outputs a discretized conductivity distribution map. Our approach leverages the power of GANs to eliminate artifacts and enhance the reconstruction of object shapes. The experimental results on the real-world dataset validate the performance of our technique. The F1 score of MITNet surpasses the state-of-the-art stacked auto-encoder (SAE) method by 5.33% on the agar data.