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
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.
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.
Fig.1Schematic diagram of open electrical impedance tomography measurement method
Fig.2Residual module of original residual network
Fig.3Multi-scale residual module
Fig.4Global 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.1Parameters of multi-scale residual network
Fig.5Loss curves of three deep learning open electrical impedance tomography algorithms
Fig.6Data set partial sample model diagram
Fig.7Partial 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.2Comparison of evaluation indexes of four open electrical impedance imaging algorithms
Fig.8Partial 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.3Comparison of evaluation indexes of different noises
Fig.9Physical drawing of measurement system hardware
Fig.10Image 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.4Comparison of evaluation indexes of actual data
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