  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.
