基于改进栈式自编码器的扩散光学层析成像逆问题求解方法研究  

Study on the inverse problem of diffuse optical tomography based on improved stacked auto-encoder

作　　者：田文旭 杨丹[1,2,3] 魏竹林 王骄[1] TIAN Wenxu;YANG Dan;WEI Zhulin;WANG Jiao(School of Information Science&Engineering,Northeastern University,Shenyang 110819,P.R.China;Key Laboratory of Infrared Optoelectric Materials and Micro-Nano Devices,Shenyang 110819,P.R.China;Key Laboratory of Data Analytics and Optimization for Smart Industry,Northeastern University,Shenyang 110819,P.R.China)

机构地区：[1]东北大学信息科学与工程学院,沈阳110819 [2]东北大学辽宁省红外光电材料及微纳器件重点实验室,沈阳110819 [3]东北大学智能工业数据解析与优化教育部重点实验室,沈阳110819

出　　处：《生物医学工程学杂志》2021年第4期774-782,共9页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(71790614);中央高校基本科研基金(2020GFZD008,2020GFYD011)。

摘　　要：扩散光学层析成像(DOT)逆问题病态性严重。传统方法成像精度不高,计算耗时,制约了DOT技术的临床应用。因此,本文提出一种基于栈式自编码器(SAE)的DOT逆问题求解方法。首先采用传统SAE方法代替迭代方法进行逆问题计算,其次改进了SAE神经网络的输出结构,使用单输出SAE降低单个网络负担,最后将改进SAE方法与传统列文伯格-马夸尔特(LM)迭代方法、传统SAE方法进行仿真比较。结果表明,本文所提方法逆问题求解平均用时只有LM迭代方法的1.67%,实验模型下均方误差(MSE)值较迭代方法降低了46.21%,较传统SAE方法降低了61.53%,图像相关系数(ICC)值较传统方法提升了4.03%,较传统SAE方法提升了18.7%,并且在3%噪声条件下具有良好的抗噪性。通过本文的研究结果证明,改进后的SAE方法相较于传统SAE方法具有更高的图像质量及抗噪性,同时相较传统迭代方法具有较快的计算速度,有利于神经网络在DOT逆问题计算中的应用。The inverse problem of diffuse optical tomography(DOT)is ill-posed.Traditional method cannot achieve high imaging accuracy and the calculation process is time-consuming,which restricts the clinical application of DOT.Therefore,a method based on stacked auto-encoder(SAE)was proposed and used for the DOT inverse problem.Firstly,a traditional SAE method is used to solved the inverse problem.Then,the output structure of SAE neural network is improved to a single output SAE,which reduce the burden on the neural network.Finally,the improved SAE method is used to compare with traditional SAE method and traditional levenberg-marquardt(LM)iterative method.The result shows that the average time to solve the inverse problem of the method proposed in this paper is only 1.67%of the LM method.The mean square error(MSE)value is 46.21%lower than the traditional iterative method,61.53%lower than the traditional SAE method,and the image correlation coefficient(ICC)value is 4.03%higher than the traditional iterative method,18.7%higher than the traditional SAE method and has good noise immunity under 3%noise conditions.The research results in this article prove that the improved SAE method has higher image quality and noise resistance than the traditional SAE method,and at the same time has a faster calculation speed than the traditional iterative method,which is conducive to the application of neural networks in DOT inverse problem calculation.

关 键 词：扩散光学层析成像 机器学习 栈式自编码器 逆问题 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]