Open AccessSeparating single- and multiple-scattering components in laser speckle contrast imaging of tissue blood flow
Author(s) -
Yifan Zhang,
Cheng Wang,
Shanbao Tong,
Peng Miao
Publication year - 2022
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.453412
Subject(s) - speckle pattern , scattering , imaging phantom , optics , blood flow , contrast (vision) , wishart distribution , monte carlo method , computer science , matrix (chemical analysis) , artificial intelligence , physics , materials science , mathematics , statistics , medicine , multivariate statistics , machine learning , composite material
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 in vivo blood flow. The random matrix-based method was adopted and parameterized using electric field Monte Carlo simulations and in vitro blood flow phantom experiments. The new method was further applied to in vivo 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 l t , l t is the transport mean free path) and deep layers (1 l t ∼ 7 l t ), extending LSCI to the evaluation of functional and pathological changes.