Huber迭代硬阈值算法在荧光分子断层成像中的应用研究  

Huber Iterative Hard Threshold Algorithm in an Application Study of Fluorescence Molecular Tomography

作　　者：袁娅婷 易黄建[1,2] 贺小伟[1,2] Yuan Yating;Yi Huangjian;He Xiaowei(School of Information Sciences and Technology,Northwest University,Xi’an 710127,Shaanxi,China;The Xi’an Key Laboratory of Radiomics and Intelligent Perception,Northwest University,Xi’an 710127,Shaanxi,China)

机构地区：[1]西北大学信息科学与技术学院,陕西西安710127 [2]西北大学西安市影像组学与智能感知重点实验室,陕西西安710127

出　　处：《中国激光》2024年第21期73-81,共9页Chinese Journal of Lasers

摘　　要：荧光分子断层成像(FMT)可以通过重建算法观察小动物体内荧光探针的三维分布,已成为一种前景广阔的用于临床前研究的成像技术。然而,由于反问题的病态性以及对噪声的敏感性,开发一种能够准确重建荧光源位置和形态的鲁棒算法是一个巨大挑战。传统的重建算法将l2范数作为残差项,扩大了噪声的影响,导致重建效果不佳。Huber迭代硬阈值(HIHT)算法将基于l2范数的代价函数修改为鲁棒度量函数,从而可将逆问题建模为有约束的优化问题。在存在噪声的情况下,HIHT算法可以有效降低噪声的影响,增强算法的鲁棒性。为了评估HIHT算法的性能,本科课题组进行了数值仿真实验和在体小鼠实验。实验结果表明,HIHT算法不仅能实现精确的荧光目标重建,而且提高了模型对噪声的鲁棒性。本研究可以促进FMT的临床前应用。Objective Fluorescence molecular tomography(FMT),which can observe the three-dimensional distribution of fluorescent probes in small animals via reconstruction algorithms,has become a promising imaging technology for preclinical studies.The strong scattering property of biological tissues and limited boundary measurements with noise have resulted in the FMT reconstruction problem being severely ill-posed.To solve the problem of FMT reconstruction,some studies have been conducted from different aspects,e.g.,the improvement of forward modeling and many regularization-based algorithms.Owing to the ill-posed nature and sensitivity to noise of the inverse problem,it is a challenge to develop a robust algorithm that can accurately reconstruct the location and morphology of the fluorescence source.Traditional reconstruction algorithms use the l2 error norm,which amplifies the influence of noise and leads to poor reconstruction results.Methods In this study,we applied the Huber iterative hard threshold(HIHT)algorithm to fluorescence molecular tomography.The HIHT algorithm modifies the l2 norm cost function into a robust metric function,and the inverse problem is modeled as a constrained optimization problem that is combinatorial in nature.The robust metric function combines the l1 and l2 loss functions to vary the robustness and efficiency of the algorithm by setting a user-defined tuning constant.In the presence of noise,the HIHT algorithm can effectively reduce the influence of noise and enhance the robustness of the algorithm.Results and Discussions Numerous numerical simulations and in vivo mouse experiments are conducted to evaluate the performance of the HIHT algorithm.The reconstruction performance of the HIHT algorithm is illustrated by the contrast-to-noise ratio(CNR),Dice coefficient,location error(LE),normalized mean square error(NMSE),and time.Quantitative and qualitative analyses show that the HIHT algorithm achieves the best reconstruction results in terms of the localization accuracy,spatial resolution of

关 键 词：荧光分子断层成像 压缩感知 鲁棒性 HIHT算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] Q632[自动化与计算机技术—计算机科学与技术]