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• W4283717811 abstract "Electrical impedance tomography (EIT) has been widely used in industrial and biomedical fields due to its visual and non-invasive natures. EIT reconstructions based on numerical algorithms are sensitive to the measurement noise and suffer from the low spatial resolution. In this paper, A novel image reconstruction method, as referred to V <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> D-Net is proposed inspired by deep learning method. The method consists of a trainable regularized pre-reconstructor and a multi-channel post-process convolution neural network(CNN). The pre-reconstructor learns a regularization pattern with prior information base on Newton-Raphson iteration methods, which could provide an initial medium distribution and solve the problem of parameter selection. And the multi-level CNN post-processor extracts features of initial results and reconstructs high resolution images with accurate shape information. The pre-reconstructor and deep CNN block are trained and optimized together. Compared with the traditional numerical methods and the related deep learning methods, V <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> D-Net is superior at robustness and generalization ability, while the reconstructions have more clear boundary shape and accurate impedance distribution information." @default.
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• W4283717811 date "2022-05-16" @default.
• W4283717811 modified "2023-09-25" @default.
• W4283717811 title "Shape Reconstruction for Electrical Impedance Tomography with V²D-Net Deep Convolutional Neural Network" @default.
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• W4283717811 doi "https://doi.org/10.1109/i2mtc48687.2022.9806671" @default.
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