摘要：AbstractElectrical Impedance Tomography (EIT) is a noninvasive, indirect image reconstruction technique which consists in the inference of the distribution of electrical conductivity inside a body or object from the set of electrical potentials measured on its boundary. Several methods have been used for the reconstruction of EIT images, such as Simulated Annealing, Kalman Filter, D-bar, and, more recently, Convolutional Neural Networks (CNN). An issue when using CNN is that the resulting image of the convolution process is smaller than the original input image. Besides that, the values lying on the borders of the input image are used less, hence their importance is overlooked. This problem is usually addressed by the introduction of padding, which is the addition of layers in the borders of the original input image. This work proposes the use of a doubly periodic padding, which is relevant for toroidal image problems such as the electric potential distribution measured using EIT. The CNN is trained using a database generated by numerical simulations. The resulting image reconstructions are presented for different noisy potential inputs.
