Electrical resistance tomography (ERT) is an important branch of process tomography (PT), which has been developed for decades. Image reconstruction is a critical step in ERT, where the object of reconstruction is the conductivity distribution of the measured field. Traditional algorithms cannot accurately establish the mapping between the measured voltage and conductivity distribution. With the development of machine learning, the convolutional neural network (CNN) has become a new image reconstruction method. Specific results have been achieved in ERT image reconstruction using CNNs. This study proposes a one-dimensional multi-branch convolutional neural network (1D-MBCNN) for ERT image reconstruction, which could retain the 1D spatial structure of the measured voltage and adaptively and efficiently extract feature information. COMSOL software and the PyTorch framework are used to build the dataset and train the neural network model, respectively. The advantages of the multi-branch structure and the effectiveness of the attention mechanism in ERT image reconstruction are verified by RIE and CC. We also evaluated the practicality of this method in the ERT system. Based on the results of different experiments, the method proposed in this paper has good imaging accuracy, noise resistance, generalization ability, and robustness.

电阻断层扫描（ERT）是过程断层扫描（PT）的一个重要分支，已经发展了几十年。图像重建是 ERT 的关键步骤，重建的对象是测量场的电导率分布。传统算法无法准确建立测量电压和电导率分布之间的映射关系。随着机器学习的发展，卷积神经网络（CNN）已经成为一种新的图像重建方法。在使用 CNN 的 ERT 图像重建中已经取得了具体的成果。本研究提出了一种用于ERT图像重建的一维多分支卷积神经网络（1D-MBCNN），该网络可以保留测量电压的一维空间结构，并自适应、高效地提取特征信息。COMSOL 软件和 PyTorch 框架分别用于构建数据集和训练神经网络模型。RIE和CC验证了多分支结构的优势和注意力机制在ERT图像重建中的有效性。我们还评估了这种方法在 ERT 系统中的实用性。基于不同实验的结果，本文提出的方法具有良好的成像精度、抗噪声能力、泛化能力和鲁棒性。