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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2022, Vol. 56 Issue (9): 1789-1795    DOI: 10.3785/j.issn.1008-973X.2022.09.012
    
Open electrical impedance imaging algorithm based on multi-scale residual network model
Jin-zhen LIU1,2(),Fei CHEN1,2,Hui XIONG1,2
1. School of Control Science and Engineering, Tiangong University, Tianjin 300387, China
2. Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin 300387, China
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Abstract  

An open electrical impedance tomography (OEIT) algorithm based on multi-scale residual neural network model was proposed, to improve the problems of OEIT image reconstruction algorithm, such as low imaging accuracy, sensitive to noise and large artifact area of reconstructed image. The algorithm used residual blocks with different sizes of convolution kernels to extract multi-scale features of boundary voltage. After the features were spliced, convolution was used to realize deep information fusion to obtain predicted conductivity distribution results. A model for the OEIT forward problem was built by the finite element method and a data set of "boundary voltage-conductivity distribution" was constructed. The proposed algorithm was compared with other algorithms in the data set and actual model experiments. Results show that the reconstruction accuracy, anti-noise ability and target location accuracy of OEIT are improved significantly by using the proposed algorithm, while the artifact area of the target is reduced.

Key words： open electrical impedance tomography (OEIT)      image reconstruction      deep learning      residual network      multi-scale feature     
Received: 18 September 2021      Published: 28 September 2022
CLC:  R 318.0  
Fund:  天津市教委科研计划项目（2019KJ014）
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Jin-zhen LIU
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Cite this article:

Jin-zhen LIU,Fei CHEN,Hui XIONG. Open electrical impedance imaging algorithm based on multi-scale residual network model. Journal of ZheJiang University (Engineering Science), 2022, 56(9): 1789-1795.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.09.012     OR     https://www.zjujournals.com/eng/Y2022/V56/I9/1789


多尺度残差网络模型的开放式电阻抗成像算法

针对开放式电阻抗成像（OEIT）的图像重建算法存在的成像精度低、对噪声敏感、重建图像伪影面积较大等问题，提出基于多尺度残差网络模型的OEIT算法. 该算法利用不同尺寸卷积核的残差块提取边界电压的多尺度特征；在完成特征拼接后，利用卷积实现深层信息融合，得到预测的电导率分布结果. 使用有限元法搭建OEIT正问题模型，构造“边界电压?电导率分布”数据集，将所提算法与其他算法在该数据集和实际模型实验中进行比较. 结果表明，所提算法使OEIT的重建精度、抗噪能力和定位目标准确性显著提高，并使检测目标的伪影面积缩小.


关键词： 开放式电阻抗成像（OEIT）,  图像重建,  深度学习,  残差网络,  多尺度特征 
Fig.1 Schematic diagram of open electrical impedance tomography measurement method
Fig.2 Residual module of original residual network
Fig.3 Multi-scale residual module
Fig.4 Global structure of multi-scale residual network
组别 c1 c2 n
1 27, 25, 23 25 16
2 21, 19, 17 19 32
3 15, 13, 11 13 32
4 9, 7, 5 7 64
Tab.1 Parameters of multi-scale residual network
Fig.5 Loss curves of three deep learning open electrical impedance tomography algorithms
Fig.6 Data set partial sample model diagram
Fig.7 Partial models and their reconstructed images for four open electrical impedance imaging algorithms
算法 $\overline {{\rm{RIE}}} $ $\overline { {\rm{ICC} } }$ 算法 $\overline {{\rm{RIE}}} $ $\overline { {\rm{ICC} } }$
TV正则化 0.758 7 0.646 9 深度残差 0.250 7 0.860 8
1D-CNN 0.379 8 0.786 5 本研究 0.196 0 0.893 6
Tab.2 Comparison of evaluation indexes of four open electrical impedance imaging algorithms
Fig.8 Partial models and reconstructed images of four open electrical impedance tomography algorithms under different noise
算法 SNR=80 dB SNR=50 dB SNR=30 dB
$\overline { {\rm{RIE} } } $ $\overline { {\rm{ICC} } } $ $\overline { {\rm{RIE} } } $ $\overline { {\rm{ICC} } } $ $\overline { {\rm{RIE} } } $ $\overline { {\rm{ICC} } } $
TV正则化 0.808 2 0.563 6 0.845 9 0.528 7 0.877 2 0.465 5
1D-CNN 0.378 0 0.787 9 0.378 3 0.787 6 0.401 5 0.770 9
深度残差 0.251 9 0.859 9 0.254 5 0.858 4 0.349 2 0.799 4
本研究 0.197 4 0.892 6 0.200 4 0.890 7 0.278 9 0.842 3
Tab.3 Comparison of evaluation indexes of different noises
Fig.9 Physical drawing of measurement system hardware
Fig.10 Image reconstruction results of four open electrical impedance imaging algorithms
算法 $\overline { {\rm{RIE} } } $ $\overline { {\rm{ICC} } } $ 算法 $\overline { {\rm{RIE} } } $ $\overline { {\rm{ICC} } } $
TV正则化 0.812 9 0.543 3 深度残差 0.602 7 0.793 8
1D-CNN 0.664 1 0.737 4 本研究 0.556 1 0.814 3
Tab.4 Comparison of evaluation indexes of actual data
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