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

A potential method for tracking neurovascular disease progression over time in preclinical models is multiphoton fluorescence microscopy (MPM), which can image cerebral vasculature with capillary-level resolution. However, obtaining high-quality, three-dimensional images with traditional point scanning MPM is time-consuming and limits sample sizes for chronic studies. Here, we present a convolutional neural network-based (PSSR Res-U-Net architecture) algorithm for fast upscaling of low-resolution or sparsely sampled images and combine it with a segmentation-less vectorization process for 3D reconstruction and statistical analysis of vascular network structure. In doing so, we also demonstrate that the use of semi-synthetic training data can replace the expensive and arduous process of acquiring low- and high-resolution training pairs without compromising vectorization outcomes, and thus open the possibility of utilizing such approaches for other MPM tasks where collecting training data is challenging. We applied our approach to images with large fields of view from a mouse model and show that our method generalizes across imaging depths, disease states and other differences in neurovasculature. Our pretrained models and lightweight architecture can be used to reduce MPM imaging time by up to fourfold without any changes in underlying hardware, thereby enabling deployability across a range of settings.

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

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 <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.

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.

Evaluation of resonant scanning as a high-speed imaging technique for two-photon imaging of cortical vasculature

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.

Segmentation-Less, Automated, Vascular Vectorization

Recent advances in two-photon fluorescence microscopy (2PM) have allowed large scale imaging and analysis of blood vessel networks in living mice. However, extracting network graphs and vector representations for the dense capillary bed remains a bottleneck in many applications. Vascular vectorization is algorithmically difficult because blood vessels have many shapes and sizes, the samples are often unevenly illuminated, and large image volumes are required to achieve good statistical power. State-of-the-art, three-dimensional, vascular vectorization approaches often require a segmented (binary) image, relying on manual or supervised-machine annotation. Therefore, voxel-by-voxel image segmentation is biased by the human annotator or trainer. Furthermore, segmented images oftentimes require remedial morphological filtering before skeletonization or vectorization. To address these limitations, we present a vectorization method to extract vascular objects directly from unsegmented images without the need for machine learning or training. The Segmentation-Less, Automated, Vascular Vectorization (SLAVV) source code in MATLAB is openly available on GitHub. This novel method uses simple models of vascular anatomy, efficient linear filtering, and vector extraction algorithms to remove the image segmentation requirement, replacing it with manual or automated vector classification. Semi-automated SLAVV is demonstrated on three in vivo 2PM image volumes of microvascular networks (capillaries, arterioles and venules) in the mouse cortex. Vectorization performance is proven robust to the choice of plasma- or endothelial-labeled contrast, and processing costs are shown to scale with input image volume. Fully-automated SLAVV performance is evaluated on simulated 2PM images of varying quality all based on the large (1.4×0.9×0.6 mm3 and 1.6×108 voxel) input image. Vascular statistics of interest (e.g. volume fraction, surface area density) calculated from automatically vectorized images show greater robustness to image quality than those calculated from intensity-thresholded images.

Effect of vascular structure on laser speckle contrast imaging

Laser speckle contrast imaging (LSCI) is a powerful tool for non-invasive, real-time imaging of blood flow in tissue. However, the effect of tissue geometry on the form of the electric field autocorrelation function and speckle contrast values is yet to be investigated. In this paper, we present an ultrafast forward model for simulating a speckle contrast image with the ability to rapidly update the image for a desired illumination pattern and flow perturbation. We demonstrate the first simulated speckle contrast image and compare it against experimental results. We simulate three mouse-specific cerebral cortex decorrelation time images and implement three different schemes for analyzing the effects of homogenization of vascular structure on correlation decay times. Our results indicate that dissolving structure and assuming homogeneous geometry creates up to ∼ 10x shift in the correlation function decay times and alters its form compared with the case for which the exact geometry is simulated. These effects are more pronounced for point illumination and detection imaging schemes, highlighting the significance of accurate modeling of the three-dimensional vascular geometry for accurate blood flow estimates.

Entropic Control of Receptor Recycling Using Engineered Ligands

Receptor internalization by endocytosis regulates diverse cellular processes, from the rate of nutrient uptake to the timescale of essential signaling events. The established view is that internalization is tightly controlled by specific protein-binding interactions. However, recent work suggests that physical aspects of receptors influence the process in ways that cannot be explained by biochemistry alone. Specifically, work from several groups suggests that increasing the steric bulk of receptors may inhibit their uptake by multiple types of trafficking vesicles. How do biochemical and biophysical factors work together to control internalization? Here, we show that receptor uptake is well described by a thermodynamic trade-off between receptor-vesicle binding energy and the entropic cost of confining receptors within endocytic vesicles. Specifically, using large ligands to acutely increase the size of engineered variants of the transferrin receptor, we demonstrate that an increase in the steric bulk of a receptor dramatically decreases its probability of uptake by clathrin-coated structures. Further, in agreement with a simple thermodynamic analysis, all data collapse onto a single trend relating fractional occupancy of the endocytic structure to fractional occupancy of the surrounding plasma membrane, independent of receptor size. This fundamental scaling law provides a simple tool for predicting the impact of receptor expression level, steric bulk, and the size of endocytic structures on receptor uptake. More broadly, this work suggests that bulky ligands could be used to drive the accumulation of specific receptors at the plasma membrane surface, providing a biophysical tool for targeted modulation of signaling and metabolism from outside the cell.

Cationic Polymer Modified Mesoporous Silica Nanoparticles for Targeted SiRNA Delivery to HER2+ Breast Cancer

In vivo delivery of siRNAs designed to inhibit genes important in cancer and other diseases continues to be an important biomedical goal. We now describe a new nanoparticle construct that has been engineered for efficient delivery of siRNA to tumors. The construct is comprised of a 47-nm mesoporous silica nanoparticle (MSNP) core coated with a cross-linked PEI-PEG copolymer, carrying siRNA against the HER2 oncogene, and coupled to the anti-HER2 monoclonal antibody (trastuzumab). The construct has been engineered to increase siRNA blood half-life, enhance tumor-specific cellular uptake, and maximize siRNA knockdown efficacy. The optimized anti-HER2-nanoparticles produced apoptotic death in HER2 positive (HER2+) breast cancer cells grown in vitro, but not in HER2 negative (HER2-) cells. One dose of the siHER2-nanoparticles reduced HER2 protein levels by 60% in trastuzumab-resistant HCC1954 xenografts. Multiple doses administered intravenously over 3 weeks significantly inhibited tumor growth (p < 0.004). The siHER2-nanoparticles have an excellent safety profile in terms of blood compatibility and low cytokine induction, when exposed to human peripheral blood mononuclear cells. The construct can be produced with high batch-to-batch reproducibility and the production methods are suitable for large-scale production. These results suggest that this siHER2-nanoparticle is ready for clinical evaluation.