基于 V-ResNet 的电阻抗层析成像方法

电阻抗层析成像技术(EIT)因其非侵入和可视化等特性为人体肺部空间特性的监测提供了一种有效的方法。但是EIT的逆问题具有严重的非线性、病态性和欠定性,使得图像重建结果含有严重的伪影。针对上述问题,提出了一种由预映射、特征提取、深度重建以及残差去噪四个模块构成的V-ResNet的深度网络成像算法,实现对场域空间位置和电导率参数分布的重建。该算法有效地增加了前馈信息的多重传递并解决了深度网络的梯度消失问题,同时残差去噪模块有效地平滑了图像边界。采用相对误差(RE)和结构相似度(SSIM)来衡量成像质量,实验得出RE的平均值为0.14,SSIM平均值为0.96。仿真与实验结果表明,基于V-ResNet的成像算法与传统的成像算法相比,图像重建结果边界清晰,空间分辨率高。

Electrical impedance tomography method based on V-ResNet

Electrical impedance tomography ( EIT) provides an effective method for monitoring the spatial features of human lungs because of its non-invasiveness and visualization natures. However, the inverse problem of EIT has serious non-linearity, ill-posedness and indeterminate feature, which makes the reconstructed images contain serious artifacts. Aiming at the above problems, a deep network imaging algorithm of V-ResNet composed of pre-mapping module, feature extraction module, deep reconstruction module and residual denoising module is proposed in this paper, which achieves the reconstruction of the spatial position and conductivity parameter distributions of the field. This algorithm can effectively increase the feedforward information by multiple transmissions and solve the phenomenon of gradient disappearance in deep networks. Meanwhile, the residual denoising module is utilized to effectively smooth the image boundary. The relative error (RE) and structural similarity (SSIM) are used to evaluate the imaging quality, and the experiments show that the average RE is 0. 14 and the average SSIM is 0. 96. The results of the simulations and experiments illustrate that compared with traditional imaging algorithms, the imaging algorithm based on V-ResNet achieves clearer boundaries and higher resolution in imaging result.