Extracting single- and multiple-scattering components in laser speckle contrast imaging of tissue blood flow

TL;DR

This paper introduces a novel random matrix theory-based method to separate single- and multiple-scattering components in laser speckle contrast imaging, enhancing the ability to detect blood flow changes in tissue.

Contribution

The study adapts and applies a random matrix approach to LSCI, enabling improved separation of scattering components and better imaging of blood flow dynamics in vivo.

Findings

Effective separation of scattering components demonstrated in vivo

Enhanced detection of functional blood flow changes

Comparison shows advantages over traditional LASCA

Abstract

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 through 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 adapted and parameterized using electric field Monte Carlo simulations and in-vitro blood flow phantom experiments. The new method was further applied in in-vivo experiments, demonstrating the benefits of separating the single- and multiple-scattering components, and was compared with the traditional temporal LASCA method. More specifically, the new method captures stimulus-induced functional changes in blood flow and tissue perfusion in the superficial and deeper layers. The new method extends the ability of LSCI to image functional and pathological changes.