基于迭代优化展开的Cherenkov激发的荧光扫描成像重建算法  

A Reconstruction Algorithm for Cherenkov⁃Excited Luminescence Scanning Imaging Based on Unrolled Iterative Optimization

作　　者：耿梦凡 张虎 李哲 胡婷 贾克斌 孙中华 冯金超 Geng Mengfan;Zhang Hu;Li Zhe;Hu Ting;Jia Kebin;Sun Zhonghua;Feng Jinchao(Beijing Key Laboratory of Computational Intelligence and Intelligent System,Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Beijing Laboratory of Advanced Information Networks,Beijing 100124,China)

机构地区：[1]北京工业大学信息学部计算智能与智能系统北京市重点实验室,北京100124 [2]先进信息网络北京实验室,北京100124

出　　处：《中国激光》2023年第15期50-60,共11页Chinese Journal of Lasers

基　　金：国家自然科学基金(81871394,82171992,62105010)。

摘　　要：Cherenkov激发的荧光扫描成像(CELSI)是一种新型的光学成像技术,为监测体内恶性肿瘤的生物学特性提供了一种手段.为提高CELSI图像重建质量,本文提出了一种基于迭代优化展开的深度学习图像重建算法——ADMM-Net.在该算法中,交替方向乘子法(ADMM)与卷积神经网络(CNN)相结合组成一个深度网络,网络中的所有参数通过端到端训练进行学习.实验结果表明:该算法可以有效提升重建图像的质量.当网络层数为5时,该算法重建的单荧光目标图像的平均峰值信噪比和结构相似性值分别可达到33.75dB和0.86.该算法不仅可以分辨出边沿距离最小为2 mm的双荧光目标,而且在多荧光目标和不同荧光量子产额比率下表现出了良好的泛化能力.Objective Cherenkov-excited luminescence scanning imaging(CELSI)is an emerging optical imaging technology that provides a new tool for tumor diagnosis and treatment.However,CELSI image reconstruction is ill-posed and underdetermined because of light scattering in biological tissues and limited boundary measurements.Regularization techniques have been widely adopted to alleviate the ill-posedness of the CELSI reconstruction.However,these methods typically exhibit poor image quality.To date,deep-learningbased reconstruction algorithms have attracted significant attention in optical tomography.To enhance the image quality of CELSI,we develop a reliable and effective deep learning reconstruction algorithm based on unrolled iterative optimization.Methods In this paper,a deep learning reconstruction algorithm is introduced based on unrolled iterative optimization,which takes the acquired sinogram image as network input and directly outputs the high-quality reconstructed images through end-to-end training.First,the image reconstruction of CELSI is reformulated as a l1 norm optimization problem based on sparse regularization technique.Second,the alternating direction method of multipliers(ADMM)based neural network algorithm(ADMM-Net)is adopted to minimize the optimization problem,which converts each iteration into convolution neural network(CNN)processing layer and deploys multiple processing layers cascaded into a deep network.Each processing layer consists of a reconstruction layer,a nonlinear layer,and a multiplier update layer.We linearize the reconstruction layer to avoid matrix inversion.The nonlinear transformation function in the nonlinear layer consists of five convolutional operators with three rectified linear unit(ReLU).The first convolution operator comprises 32 filters with the size of 3×3,and the other convolution operators consist of 32 filters with the size of 3×3×32.Note that all the parameters in the ADMM-Net are end-to-end updated through gradient backpropagation,including the step size and regular

关 键 词：医用光学 生物技术 Cherenkov激发的荧光扫描成像 图像重建技术 交替方向乘子法 深度学习 优化展开 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]