MITNet：基于复合CNN的GAN增强磁感应断层成像
MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNN
摘要：
磁感应断层成像（MIT）是一种有效的脑疾病长期监测解决方案，其重点是利用非侵入式电磁场重建人脑内的生物阻抗分布。然而，高质量的脑图像重建仍然具有挑战性，因为从测量的弱信号重建图像是一个高度非线性和病态的问题。在这项工作中，我们提出了一个生成对抗网络（GAN）增强MIT技术，命名为MITNet，基于复杂的卷积神经网络（CNN）。在真实世界数据集上的实验结果验证了我们的技术的性能，比最先进的方法高出25.27%。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
