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.

Open electrical impedance imaging algorithm based on multi-scale residual network model

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.