MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNN
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Abstract (arXiv)
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%.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 74 references that are in the map. First posted on arXiv 16 Feb 2021.
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19 of 74 references.
References · 19
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