An EIT image reconstruction method based on DenseNet with multi-scale convolution

Dan Yang; Shijun Li; Yuyu Zhao; Bin Xu; Wenxu Tian; Dan Yang; Shijun Li; Yuyu Zhao; Bin Xu; Wenxu Tian

doi:10.3934/mbe.2023329

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 4: 7633-7660. doi: 10.3934/mbe.2023329

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Research article

An EIT image reconstruction method based on DenseNet with multi-scale convolution

1.
Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang 110819, China
2.
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
3.
School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China

Academic Editor: Yang Kuang
^† The authors contributed equally.

Received: 05 November 2022 Revised: 14 January 2023 Accepted: 05 February 2023 Published: 20 February 2023

Electrical impedance tomography (EIT) is an imaging technique that non-invasively acquires the electrical conductivity distribution within a field. The ill-posed and nonlinear nature of the image reconstruction process results in lower quality of the obtained images. To solve this problem, an EIT image reconstruction method based on DenseNet with multi-scale convolution named MS-DenseNet is proposed. In the proposed method, three different multi-scale convolutional dense blocks are incorporated to replace the conventional dense blocks; they are placed in parallel to improve the generalization ability of the network. The connection layer between dense blocks adopts a hybrid pooling structure, which reduces the loss of information in the traditional pooling process. A learning rate setting achieves reduction in two stages and optimizes the fitting ability of the network. The input of the constructed network is the boundary voltage data, and the output is the conductivity distribution of the imaging area. The network was trained and tested on a simulated dataset, and it was further tested using actual measurement data. The images reconstructed via this method were evaluated by employing root mean square error, structural similarity index measure, mean absolute error and image correlation coefficient in comparison with conventional DenseNet and Gauss-Newton. The results show that the method improves the artifact and edge blur problems, achieves higher values on the image metrics and improves the EIT image quality.
- electrical impedance tomography,
- image reconstruction,
- DenseNet,
- multi-scale convolution,
- hybrid pooling structure
Citation: Dan Yang, Shijun Li, Yuyu Zhao, Bin Xu, Wenxu Tian. An EIT image reconstruction method based on DenseNet with multi-scale convolution[J]. Mathematical Biosciences and Engineering, 2023, 20(4): 7633-7660. doi: 10.3934/mbe.2023329

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Abstract

Electrical impedance tomography (EIT) is an imaging technique that non-invasively acquires the electrical conductivity distribution within a field. The ill-posed and nonlinear nature of the image reconstruction process results in lower quality of the obtained images. To solve this problem, an EIT image reconstruction method based on DenseNet with multi-scale convolution named MS-DenseNet is proposed. In the proposed method, three different multi-scale convolutional dense blocks are incorporated to replace the conventional dense blocks; they are placed in parallel to improve the generalization ability of the network. The connection layer between dense blocks adopts a hybrid pooling structure, which reduces the loss of information in the traditional pooling process. A learning rate setting achieves reduction in two stages and optimizes the fitting ability of the network. The input of the constructed network is the boundary voltage data, and the output is the conductivity distribution of the imaging area. The network was trained and tested on a simulated dataset, and it was further tested using actual measurement data. The images reconstructed via this method were evaluated by employing root mean square error, structural similarity index measure, mean absolute error and image correlation coefficient in comparison with conventional DenseNet and Gauss-Newton. The results show that the method improves the artifact and edge blur problems, achieves higher values on the image metrics and improves the EIT image quality.

References

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