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%.
@article{arxiv.2102.07911,
title = {MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNN},
author = {Zuohui Chen and Qing Yuan and Xujie Song and Cheng Chen and Dan Zhang and Yun Xiang and Ruigang Liu and Qi Xuan},
journal= {arXiv preprint arXiv:2102.07911},
year = {2021}
}