Fast Hyperspectral Diffuse Optical Imaging Method with Joint Sparsity
Fast Hyperspectral Diffuse Optical Imaging Method with Joint Sparsity
复制标题
具有联合稀疏性的快速高光谱漫反射光学成像方法
DOI：
10.1109/embc.2019.8857069
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Jing Qin
中科院分区：
文献类型：
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作者：
Natalie Durgin;Rachel Grotheer;Chenxi Huang;S. Li;A. Ma;D. Needell;Jing Qin
漫射光学断层扫描（DOT）是临床诊断和治疗中重要的功能成像方式。随着所获取的DOT数据中波长的增加，重建组织（即DOT图像）的扩散和吸收系数变得非常具有挑战性。本文利用待重构图像的联合稀疏性，将高光谱DOT （hyDOT）反问题视为多测量向量（MMV）问题。然后，我们提出了一种基于MMV随机梯度匹配追踪（MStoGradMP）和小批处理技术的快速随机贪心算法。数值结果表明，该算法与相关的稀疏正则化梯度下降法相比，重构精度更高，运行时间显著缩短。
Diffuse optical tomography (DOT) is an important functional imaging modality in clinical diagnosis and treatment. As the number of wavelengths in the acquired DOT data grows, it becomes very challenging to reconstruct diffusion and absorption coefficients of tissue, i.e., a DOT image. In this paper, we consider the hyperspectral DOT (hyDOT) inverse problem as a multiple-measurement vector (MMV) problem by exploiting the joint sparsity of the images to be reconstructed. Then we propose a fast stochastic greedy algorithm based on the MMV stochastic gradient matching pursuit (MStoGradMP) and the mini-batching technique. Numerical results show that the proposed algorithm can achieve higher reconstruction accuracy with significantly reduced running time than the related gradient descent method with sparsity regularization.