High-resolution three-dimensional blood flow tomography in the subdiffuse regime using laser speckle contrast imaging

Abstract

Significance: Visualizing 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. Aim: We 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. Approach: Our 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. Results: We 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 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 and beyond. Conclusions: We 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.

A Deep Learning Approach for Improving Two-Photon Vascular Imaging Speeds

“…Another area of research that could benefit from increased imaging speeds is the study of light propagation through the brain for the development of noninvasive brain imaging devices. With accessibility to a larger database of large-FOV two-photon images, light propagation models can be more thoroughly developed, tested, and refined [32]. As precise capillary capture is not necessary for these models, the use of even lower resolution and faster imaging could be further explored with PSSR.…”

A deep learning approach for improving two-photon vascular imaging speeds

“…Another area of research that could benefit from increased imaging speeds is in the study of light propagation through the brain, for the development of noninvasive brain imaging devices. With accessibility to a larger database of large FOV two-photon images, light propagation models can be more thoroughly developed, tested, and refined 27 . As precise capillary capture is not necessary for these models, the use of even lower resolution and faster imaging could be further explored with PSSR.…”

SpeckleCam: High-resolution Computational Speckle Contrast Tomography for Deep Blood Flow Imaging

“…Reconstructing for many voxels is not feasible due to the matrix-inversion step and therefore limits the spatial resolution of the reconstructed volume [25]. In [28], the authors tackle the poor resolution issue by using a high-resolution structural prior volume obtained via two-photon microscopy. A perturbation Monte Carlo model is then used to recover blood flow in high resolution.…”