We demonstrate a simple, low-cost two-photon microscope design with both galvo-galvo and resonant-galvo scanning capabilities. We quantify and compare the signal-to-noise ratios and imaging speeds of the galvo-galvo and resonant-galvo scanning modes when used for murine neurovascular imaging. The two scanning modes perform as expected under shot-noise limited detection and are found to achieve comparable signal-to-noise ratios. Resonant-galvo scanning is capable of reaching desired signal-to-noise ratios using less acquisition time when higher excitation power can be used. Given equal excitation power and total pixel dwell time between the two methods, galvo-galvo scanning outperforms resonant-galvo scanning in image quality when detection deviates from being shot-noise limited.

Here we introduce a fiber amplifier and a diamond Raman laser that output high powers (6.5 W, 1.3 W) at valuable wavelengths (1060 nm, 1250 nm) for two-photon excitation of red-shifted fluorophores. These custom excitation sources are both simple to construct and cost-efficient in comparison to similar custom and commercial alternatives. Furthermore, they operate at a repetition rate (80 MHz) that allows fast image acquisition using resonant scanners. With our system we demonstrate compatibility with fast resonant scanning, the ability to acquire neuronal images, and the capability to image vasculature at deep locations (>1 mm) within the mouse cerebral cortex.

Monitoring blood flow is critical to treatment efficacy in many surgical settings. Laser speckle contrast imaging (LSCI) is a simple, real-time, label-free optical technique for monitoring blood flow that has emerged as a promising technique but lacks the ability to make repeatable quantitative measurements. Multi-exposure speckle imaging (MESI) is an extension of LSCI that requires increased complexity of instrumentation, which has limited its adoption. In this paper, we design and fabricate a compact, fiber-coupled MESI illumination system (FCMESI) that is substantially smaller and less complex than previous systems. Using microfluidics flow phantoms, we demonstrate that the FCMESI system measures flow with an accuracy and repeatability equivalent to traditional free space MESI illumination systems. With an in vivo stroke model, we also demonstrate the ability of FCMESI to monitor cerebral blood flow changes.

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.

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