  Magnetic induction tomography (MIT) is an efficient solution for long-term
brain disease monitoring, which focuses on reconstructing bio-impedance
distribution inside the human brain using non-intrusive electromagnetic fields.
However, high-quality brain image reconstruction remains challenging since
reconstructing images from the measured weak signals is a highly non-linear 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). The experimental results on the real-world
dataset validate the performance of our technique, which outperforms the
state-of-art method by 25.27%.
