Simulation study of brain electrical impedance tomography based on radial basis function neural network
10.19745/j.1003-8868.2024185
- VernacularTitle:基于径向基函数神经网络的脑部电阻抗断层成像仿真研究
- Author:
Tao ZHANG
1
;
Xin-Yi WANG
;
Jiang-Hui HAO
;
Lei LIANG
;
Can-Hua XU
;
Feng FU
;
Xue-Chao LIU
Author Information
1. 西宁联勤保障中心药品仪器监督检验站,兰州 730050
- Keywords:
electrical impedance tomography;
brain electrical impedance tomography;
radial basis function neural network;
image reconstruction;
simulation of electrical impedance tomography
- From:
Chinese Medical Equipment Journal
2024;45(10):1-6
- CountryChina
- Language:Chinese
-
Abstract:
Objective To study the ability of radial basis function neural network(RBFNN)with different implementations for electrical impedance tomography(EIT)under real brain shapes,to evaluate the advantages and disadvantages of different approaches,and to provide a reference for the selection of practical imaging methods.Methods COMSOL Multiphysics was used to establish a multilayer 2D model with real structure based on brain CT and an EIT simulation dataset.The effects of the exact RBFNN,the orthogonal least squares-based RBFNN(OLS RBFNN)and the K-Means-based BRFNN(K-Means RBFNN)on the image reconstruction result were explored with the dataset constructed.The root mean square error(RMSE)and image correlation coefficient(ICC)were adopted to evaluate the imaging results.Results EIT could be completed with all the three RBFNNs without noise,and the exact RBFNN had the best results with average ICC and RMSE of 0.784 and 0.467,respectively,in the test set.The OLS RBFNN had the best imaging results at a hidden node of 50,with an average ICC and RMSE of 0.788 and 0.462,respectively.The K-Means RBFNN achieved the best imaging results at noise levels of 30,40,50,60,70 and 80 dB with stable ICC and RMSE and high robustness.Conclusion All the three RBFNNs can be used for brain EIT image reconstruction with their own advantages and disadvantages,and the RBFNN has to be selected for EIT reconstruc-tion based on considerations on actual conditions.[Chinese Medical Equipment Journal,2024,45(10):1-6]
