Liu Jinzhen, Chen Liming, Xiong Hui, Zhang Liying
The School of Control Science and Engineering, Tiangong University, Tianjin 300387, People's Republic of China.
Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin 300387, People's Republic of China.
Rev Sci Instrum. 2024 Mar 1;95(3). doi: 10.1063/5.0176494.
Electrical impedance tomography (EIT), a non-invasive, radiation-free, and convenient imaging technique, has been widely used in the diagnosis of stroke. However, due to soft-field nonlinearity and the ill-posed inverse problem, EIT images always suffer from low spatial resolution. Therefore, a multi-scale convolutional attention residual-based U-Net (MARU-Net) network is proposed for stroke reconstruction. Based on the U-Net network, a residual module and a multi-scale convolutional attention module are added to the concatenation layer. The multi-scale module extracts feature information of different sizes, the attention module strengthens the useful information, and the residual module improves the performance of the network. Based on the above advantages, the network is used in the EIT system for stroke imaging. Compared with convolutional neural networks and one-dimensional convolutional neural networks, the MARU-Net network has fewer artifacts, and the reconstructed image is clear. At the same time, the reduction of noisy artifacts in the MARU-Net network is verified. The results show that the image correlation coefficient of the reconstructed image with noise is greater than 0.87. Finally, the practicability of the network is verified by a model physics experiment.
电阻抗断层成像（EIT）是一种无创、无辐射且便捷的成像技术，已广泛应用于中风诊断。然而，由于软场非线性和不适定逆问题，EIT图像的空间分辨率一直较低。因此，提出了一种基于多尺度卷积注意力残差的U-Net（MARU-Net）网络用于中风重建。在U-Net网络的基础上，在拼接层添加了残差模块和多尺度卷积注意力模块。多尺度模块提取不同大小的特征信息，注意力模块强化有用信息，残差模块提升网络性能。基于上述优势，该网络应用于EIT系统进行中风成像。与卷积神经网络和一维卷积神经网络相比，MARU-Net网络的伪影更少，重建图像清晰。同时，验证了MARU-Net网络中噪声伪影的减少。结果表明，有噪声的重建图像的图像相关系数大于0.87。最后，通过模型物理实验验证了该网络的实用性。
