论文标题
基于学习的3D EIT图像重建方法
A Learning-Based 3D EIT Image Reconstruction Method
论文作者
论文摘要
深度学习已被广泛用于解决电阻抗断层扫描（EIT）图像重建问题。大多数现有的基于物理模型和基于学习的方法都集中在2D EIT图像重建上。但是，当它们直接扩展到3D域时，几乎不能保证在图像质量和噪声稳健性方面的重建性能主要是由于维度的显着增加。本文提出了一种基于学习的3D EIT图像重建方法，该方法称为神经元网络（TN-NET）的转置卷积。模拟和实验结果表明，与流行的3D EIT图像重建算法相比，TN-NET的性能和概括能力出色。
Deep learning has been widely employed to solve the Electrical Impedance Tomography (EIT) image reconstruction problem. Most existing physical model-based and learning-based approaches focus on 2D EIT image reconstruction. However, when they are directly extended to the 3D domain, the reconstruction performance in terms of image quality and noise robustness is hardly guaranteed mainly due to the significant increase in dimensionality. This paper presents a learning-based approach for 3D EIT image reconstruction, which is named Transposed convolution with Neurons Network (TN-Net). Simulation and experimental results show the superior performance and generalization ability of TN-Net compared with prevailing 3D EIT image reconstruction algorithms.
