The boundary artifacts and low-spatial resolution in reconstruction due to the ‘soft-field’ and the ill-posed nature of the inverse problems imaging with electrical tomography ( ET) are considered. This article designs a novel deep learning-based ET image reconstruction framework consisted of an unrolling iteration pre-reconstructor and a modified attention-based deep convolutional neural network ( CNN) postprocessor. Specifically, the pre-reconstructor, a four-layers deconvolution network, is unrolled by the NewtonRaphson algorithm. The U-Net is the backbone of the post-processor and two carefully designed feature connections are introduced. Firstly, the residual connection is added to the feature extraction and image reconstruction block which could alleviate the reverse gradient vanishing problems. Secondly, the residual self-attention skip connections are proposed which could better fuse the global and local information. These above-mentioned strategies can better express the nonlinear characteristics of ET inverse problems. The visual results show that the reconstruction using the proposed methods has higher spatial resolution and more clear shape representation (i. e. , sharper boundary features and clear medium distributions). The quantity results (RE= 0. 10 and CC= 0. 93 in test performance) indicate that the proposed method could improve the imaging results effectively. A reliable method for nondestructive measurement and visualization is promoted.