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arXiv · 2102.07911

MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNN

Zuohui Chen·Qing Yuan·Xujie Song·Cheng Chen·Dan Zhang·Yun Xiang·Ruigang Liu·Qi Xuan
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Abstract

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%.

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Zuohui Chen, Qing Yuan, Xujie Song, Cheng Chen, Dan Zhang, Yun Xiang, Ruigang Liu, Qi Xuan. 2021-02-16. MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNN. https://arxiv.org/abs/2102.07911

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