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Quantitative blood flow velocity imaging using laser speckle flowmetry.

ABSTRACT: Laser speckle flowmetry suffers from a debated quantification of the inverse relation between decorrelation time (?c) and blood flow velocity (V), i.e. 1/?c?=??V. Using a modified microcirculation imager (integrated sidestream dark field - laser speckle contrast imaging [SDF-LSCI]), we experimentally investigate on the influence of the optical properties of scatterers on ? in vitro and in vivo. We found a good agreement to theoretical predictions within certain limits for scatterer size and multiple scattering. We present a practical model-based scaling factor to correct for multiple scattering in microcirculatory vessels. Our results show that SDF-LSCI offers a quantitative measure of flow velocity in addition to vessel morphology, enabling the quantification of the clinically relevant blood flow, velocity and tissue perfusion.

SUBMITTER: Nadort A

PROVIDER: S-EPMC4850477 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Quantitative blood flow velocity imaging using laser speckle flowmetry.

Nadort Annemarie A Kalkman Koen K van Leeuwen Ton G TG Faber Dirk J DJ

Scientific reports 20160429

Laser speckle flowmetry suffers from a debated quantification of the inverse relation between decorrelation time (τc) and blood flow velocity (V), i.e. 1/τc = αV. Using a modified microcirculation imager (integrated sidestream dark field - laser speckle contrast imaging [SDF-LSCI]), we experimentally investigate on the influence of the optical properties of scatterers on α in vitro and in vivo. We found a good agreement to theoretical predictions within certain limits for scatterer size and mult ...[more]

PMID: 27126250

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Project description:SignificanceVisualizing high-resolution hemodynamics in cerebral tissue over a large field of view (FOV), provides important information in studying disease states affecting the brain. Current state-of-the-art optical blood flow imaging techniques either lack spatial resolution or are too slow to provide high temporal resolution reconstruction of flow map over a large FOV.AimWe present a high spatial resolution computational optical imaging technique based on principles of laser speckle contrast imaging (LSCI) for reconstructing the blood flow maps in complex tissue over a large FOV provided that the three-dimensional (3D) vascular structure is known or assumed.ApproachOur proposed method uses a perturbation Monte Carlo simulation of the high-resolution 3D geometry for both accurately deriving the speckle contrast forward model and calculating the Jacobian matrix used in our reconstruction algorithm to achieve high resolution. Given the convex nature of our highly nonlinear problem, we implemented a mini-batch gradient descent with an adaptive learning rate optimization method to iteratively reconstruct the blood flow map. Specifically, we implemented advanced optimization techniques combined with efficient parallelization and vectorization of the forward and derivative calculations to make reconstruction of the blood flow map feasible with reconstruction times on the order of tens of minutes.ResultsWe tested our reconstruction algorithm through simulation of both a flow phantom model as well as an anatomically correct murine cerebral tissue and vasculature captured via two-photon microscopy. Additionally, we performed a noise study, examining the robustness of our inverse model in presence of 0.1% and 1% additive noise. In all cases, the blood flow reconstruction error was <2 % for most of the vasculature, except for the peripheral vasculature which suffered from insufficient photon sampling. Descending vasculature and deeper structures showed slightly higher sensitivity to noise compared with vasculature with a horizontal orientation at the more superficial layers. Our results show high-resolution reconstruction of the blood flow map in tissue down to 500 μm and beyond.ConclusionsWe have demonstrated a high-resolution computational imaging technique for visualizing blood flow map in complex tissue over a large FOV. Once a high-resolution structural image is captured, our reconstruction algorithm only requires a few LSCI images captured through a camera to reconstruct the blood flow map computationally at a high resolution. We note that the combination of high temporal and spatial resolution of our reconstruction algorithm makes the solution well-suited for applications involving fast monitoring of flow dynamics over a large FOV, such as in functional neural imaging.

Dataset Information

Quantitative blood flow velocity imaging using laser speckle flowmetry.

Publications

Quantitative blood flow velocity imaging using laser speckle flowmetry.

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