Zhang Yifan, Wang Cheng, Tong Shanbao, Miao Peng
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.
Biomed Opt Express. 2022 Apr 21;13(5):2881-2895. doi: 10.1364/BOE.453412. eCollection 2022 May 1.
Random matrix theory provides new insights into multiple scattering in random media. In a recent study, we demonstrated the statistical separation of single- and multiple-scattering components based on a Wishart random matrix. The first- and second-order moments were estimated with a Wishart random matrix constructed using dynamically backscattered speckle images. In this study, this new strategy was applied to laser speckle contrast imaging (LSCI) of blood flow. The random matrix-based method was adopted and parameterized using electric field Monte Carlo simulations and blood flow phantom experiments. The new method was further applied to experiments, demonstrating the benefits of separating the single- and multiple-scattering components, and the method was compared with the traditional temporal laser speckle contrast analysis (LASCA) method. More specifically, the new method separates the stimulus-induced functional changes in blood flow and tissue perfusion in the superficial (<2 , is the transport mean free path) and deep layers (1 ∼ 7 ), extending LSCI to the evaluation of functional and pathological changes.
随机矩阵理论为随机介质中的多重散射提供了新的见解。在最近的一项研究中，我们基于威沙特随机矩阵证明了单散射和多重散射分量的统计分离。利用动态反向散射散斑图像构建的威沙特随机矩阵估计了一阶和二阶矩。在本研究中，这种新策略被应用于血流的激光散斑对比成像（LSCI）。采用基于随机矩阵的方法，并通过电场蒙特卡罗模拟和血流模型实验进行参数化。该新方法进一步应用于实验，证明了分离单散射和多重散射分量的好处，并将该方法与传统的时间激光散斑对比分析（LASCA）方法进行了比较。更具体地说，新方法分离了浅表（<2 ， 是输运平均自由程）和深层（1 ∼ 7 ）中刺激引起的血流和组织灌注的功能变化，将LSCI扩展到功能和病理变化的评估。
