肺部电阻抗成像电极阵列优化方法研究

Optimization of electrode array for lung electrical impedance imaging

肺部电阻抗层析成像(EIT)电极阵列的设计是影响系统性能与成像效果的关键因素之一,目前多在规则形状物场、等间距分布的前提条件下对电极阵列进行优化,却并不适用于肺部不规则边界的情况。本文提出基于深度学习的肺部电阻抗层析成像电极阵列优化方法,以电极位置为优化目标,以重建图像相对误差、图像相关系数、敏感场分布的均匀性以及敏感场Hessian矩阵的条件数为网络输入,以阵列电极位置为网络输出,基于DNN网络构建优化模型。实验结果表明,在呼气末和吸气末两种状态下,与传统的电极阵列均匀分布方法相比,基于深度学习的肺部EIT电极阵列优化方法将重建图像相关系数(image correlation coefficient,ICC)分别提高了33.17%、33.86%,结构相似度(structural similarity,SSIM)分别提高了14.5%、14.39%,峰值信噪比(peak signal-to-noise ratio,PSNR)分别提高了26.3%、28.27%。因此可以得出结论,与传统方法相比基于深度学习的EIT电极阵列优化方法更适用于肺部EIT成像。

The design of electrical impedance tomography(EIT)electrode array is one of the key factors affecting the performance and imaging effect of the system.At present,the electrode array is optimized under the premise of regular shape field and equal spacing distribution which is not suitable for irregular lung boundaries.In this paper,an optimization method of electrode array based on deep learning network is proposed for lung EIT.The optimization goal of the network is electrode position.The relative error of the reconstructed image,the image correlation coefficient,the distribution uniformity and the condition number of the Hessian matrix for the sensitive field are used as the network inputs.The positions of the electrodes are taken as the network output.The optimization model is constructed based on DNN network.The experimental results show that,for end-expiration and end-inspiration states,the ICC,SSIM and PSNR of images reconstructed based on measured data obtained from optimized electrode increased by 33.17%and 33.86%,14.5%and 14.39%,26.3%and 28.27%,respectively,compared with the traditional electrode array with equal-distance distribution.Therefore,it can be concluded that optimizing electrode positions for lung EIT using deep learning is more suitable than traditional methods.