Abstract
We introduce an integration of dynamic light scattering (DLS) and optical coherence tomography (OCT) for high-resolution 3D imaging of heterogeneous diffusion and flow. DLS analyzes fluctuations in light scattered by particles to measure diffusion or flow of the particles, and OCT uses coherence gating to collect light only scattered from a small volume for high-resolution structural imaging. Therefore, the integration of DLS and OCT enables high-resolution 3D imaging of diffusion and flow. We derived a theory under the assumption that static and moving particles are mixed within the OCT resolution volume and the moving particles can exhibit either diffusive or translational motion. Based on this theory, we developed a fitting algorithm to estimate dynamic parameters including the axial and transverse velocities and the diffusion coefficient. We validated DLS-OCT measurements of diffusion and flow through numerical simulations and phantom experiments. As an example application, we performed DLS-OCT imaging of the living animal brain, resulting in 3D maps of the absolute and axial velocities, the diffusion coefficient, and the coefficient of determination.
Highlights
Dynamic light scattering (DLS) is widely used to quantify the dynamics of scattering particles by analyzing the autocorrelation function of light scattered from the particles [1,2,3]
This paper describes the integration of DLS and Optical coherence tomography (OCT) leading to a novel technique for high-resolution 3D imaging of particle dynamics, named DLS-OCT
This paper describes the derivation of the DLS-OCT theory, from the OCT signal when a single particle moves, to the field autocorrelation function when many particles exhibit heterogeneous dynamics
Summary
Dynamic light scattering (DLS) is widely used to quantify the dynamics of scattering particles by analyzing the autocorrelation function of light scattered from the particles [1,2,3]. This paper describes the integration of DLS and OCT leading to a novel technique for high-resolution 3D imaging of particle dynamics, named DLS-OCT For this integration, we propose and validate a theory that considers three concerns simultaneously. The theory considers the non-ergodic effect, since static and moving particles can be mixed within the OCT resolution volume in a highly heterogeneous sample As both diffusion and flow can appear in a sample though spatially separated (e.g., the brain where both translational blood flow and diffusive intracellular organelle motions appear), the theory should be able to distinguish diffusive motions from translational flow and measure either the diffusion coefficient or the velocity according to the distinction. We derived the field autocorrelation function directly from the phase-resolved OCT signal under the assumption that static and moving particles are mixed in the resolution volume of OCT and the moving particles can exhibit either diffusive or translational motion
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