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| Reconstruction of Thorax Image Based on Deep CG Method for Electrical Impedance Tomography |
| Wang Zichen, Fu Rong, Zhang Xinyu, Wang Di, Chen Xiaoyan* |
| (College of Electronic Information and Automatic, Tianjin University of Science and Technology, Tianjin 300222, China) |
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Abstract To improve the spatial resolution of electrical impedance tomography, a novel method based on conjugate gradient (CG) with rapid pre-reconstruction and deep stack autoencoder post-processing was proposed (Deep CG). The core idea is that the merging ofnumerical reconstruction algorithm with deeplearning-based method makes the structure and conductivity distribution of the thorax more accurate. Firstly, the mathematical reconstruction algorithm CG was adopted to pre-reconstruct the coarse image, and the mapping between boundary voltage and conductivity distribution in the chest was achieved. Next, to take full advantages of the different spatial features, the stack autoencoder was employed to connect the encoding and the decoding modules hierarchically, which realized the feature extraction (FE) and the image reconstruction (IR). Finally, to train the model, the dataset was constructed from the number of 400 clinical CT slices, a mixed supervised method was employed to adjust the model parameters, which not only avoided the dispersion of the information flow and gradient flow, but alsooptimized the parameters of Deep CG method. The relative error (RE) and correlation coefficient (CC) were adopted to evaluate the image quantitively. The images were compared to the traditionalnumerical algorithm and a full connected neural network. The results showed that the RE was decreased to 0.11 from 0.5 and 0.24, and the CC was improved to 0.96 from 0.8 and 0.9. The proposed method was able to reconstruct EIT images with higher spatial resolution and clear boundary,which is expected to put forward EIT techniques to the further applications and researches.
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Received: 26 April 2021
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Corresponding Authors:
*E-mail: cxywxr@tust.edu.cn
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