Electrical Capacitance Tomography (ECT) image reconstruction has been largely applied for industrial applications. However, there is still a crucial need to develop a new framework to enhance the quality of reconstructed images and make it faster. Deep learning has recently boomed and applied in many fields since it is good at mapping complicated nonlinear functions based on series of artificial neural networks. In this paper, a novel image reconstruction method based on a deep neural network is proposed. The proposed image reconstruction algorithm mainly uses Long Short-Term Memory (LSTM) deep neural network, which is abbreviated as LSTM-IR algorithm. A big simulation dataset containing 160k pairs of instances is created to train and test the performance of the proposed LSTM-IR algorithm. Each pair of the sample has a predefined permittivity distribution vector and corresponding capacitance vector. The generalization ability and feasibility of the LSTM-IR network are measured using contaminated data, data not included in the training dataset, and experimental data. The preliminary results show that the proposed LSTM-IR method can create fast and more accurate ECT images than traditional and deep learning image reconstruction algorithms.

中文翻译：

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基于深度神经网络的电容层析成像图像重建

电容断层扫描 (ECT) 图像重建已广泛应用于工业应用。然而，仍然迫切需要开发一个新的框架来提高重建图像的质量并使其更快。深度学习由于擅长基于一系列人工神经网络映射复杂的非线性函数，因此近年来在许多领域得到蓬勃发展和应用。在本文中，提出了一种基于深度神经网络的新型图像重建方法。所提出的图像重建算法主要使用长短期记忆（LSTM）深度神经网络，简称LSTM-IR算法。创建一个包含 160k 对实例的大型模拟数据集来训练和测试所提出的 LSTM-IR 算法的性能。每对样本具有预定义的介电常数分布向量和对应的电容向量。LSTM-IR 网络的泛化能力和可行性是使用污染数据、未包含在训练数据集中的数据和实验数据来衡量的。初步结果表明，与传统和深度学习图像重建算法相比，所提出的 LSTM-IR 方法可以创建更快、更准确的 ECT 图像。