基于全连接神经网络的颅脑电阻抗成像参考电压预测方法

Research on Reference Voltage Prediction for Electrical Impedance Tomography Based on Fully Connected Neural Network

电阻抗层析成像(EIT)作为一种新兴可视化技术,可通过电导率分布变化的重建图像获得人体组织病理变化信息,为疾病检测提供了一种选择。在基于EIT的颅脑疾病检测中,为了准确获取差分成像图像重建所需的参考电压,提出一种基于全连接神经网络(FCNN)的参考电压预测方法。通过研究所提方法在不同信噪比情况下的图像重建性能,验证所提方法对参考电压预测的准确性和泛化能力。此外,还研究了头皮、颅骨和脑组织电导率分别发生变化时所提方法的有效性,并通过计算模糊半径和相关系数对图像重建质量进行了定量评价。结果表明,所提方法在一定电导率范围内和不同噪声水平下能够有效预测参考电压。

Electrical impedance tomography(EIT)is a visualization techniquetoreconstruct conductivity distribution variations that reflect pathological changes in human tissues based on the boundary voltage measurement.Difference imaging is commonly used in the reconstruction to reduce modeling errors.Cerebral hemorrhage or ischemia can cause concentration changes of the intracranial ions,affecting the conductivity distribution.Consequently,the reference voltage obtained at a specific instant is inaccurate in the difference imaging.This paper proposesa reference voltage prediction method for brain EIT by fully connecting a neural network(FCNN).The reference voltage can be accurately predicted by establishing a nonlinear mapping between the measured and reference voltages.Firstly,a three-layer brain model is constructed,including the scalp,skull,and brain tissue layers.The measured boundary voltage is used to construct the input matrix,and the true reference voltage is applied to construct the output matrix in the network.Anumber of training datasets are established to train the network.During the back-propagation of the loss function,an adaptive moment estimation algorithm is employed to update the parameters of FCNN.Then,the nonlinear relationship between the boundary measurement and the true reference voltage can be acquired,and the reference voltage can be predicted.Simulation and experiments validate the proposed method.Compared with the true reference voltage,simulation results show that the voltage relative error ranges from 0%to 0.10%under the noise-free condition and 0%to 0.15%under the noisy condition.The reference voltage predicted by the proposed method well approaches the true reference voltage.Image reconstruction is performed based on the predicted reference voltage.The results show that the simulated stroke in the brain tissue layer can be reconstructed.The average blur radius of the reconstructed image increases,and the average correlation coefficient decreases gradually when the signal-to-noise ratio decreases.The feasibility of the proposed method is also tested when the conductivity of the scalp layer,skull layer,and brain tissue layer changes.It is found that the reconstructed image is very similar to the true conductivity distribution.The phantom experiment also validates the excellent performance of the proposed method.The following conclusions can be drawn.(1)Due to the powerful mapping ability of FCNN,the proposed method can establish the nonlinear relationship between the measured boundary voltage and the true reference voltage in the brain EIT.(2)The difference between the predicted and true reference voltage is minor.The conductivity distribution of different models can be well reconstructed using the predicted reference voltage in the image reconstruction.(3)The proposed method only requires boundary measurement to obtain the information of reference voltage,avoiding the reference voltage calibration problem.