Targeted capillary photothrombosis via multiphoton excitation of Rose Bengal.

Microvascular stalling, the process occurring when a capillary temporarily loses perfusion, has gained increasing interest in recent years through its demonstrated presence in various neuropathologies. Studying the impact of such stalls on the surrounding brain tissue is of paramount importance to understand their role in such diseases. Despite efforts trying to study the stalling events, investigations are hampered by their elusiveness and scarcity. In an attempt to alleviate these hurdles, we present here a novel methodology enabling transient occlusions of targeted microvascular segments through multiphoton excitation of Rose Bengal, an established photothrombotic agent. With n = 7 mice C57BL/6 J (5 males and 2 females) and 95 photothrombosis trials, we demonstrate the ability of triggering reversible blockages by illuminating a capillary segment during ∼300 s at 1000 nm, using a standard Ti:Sapphire femtosecond laser. Furthermore, we performed concurrent Optical Coherence Microscopy (OCM) angiography imaging of the microvascular network to highlight the specificity of the targeted occlusion and its duration. Through comparison with a control group, we conclude that blood flow cessation is indeed created by the photothrombotic agent via multiphoton excitation and is temporary, followed by a flow recovery in less than 24 h. Moreover, Immunohistology points toward a stalling mechanism driven by adherence of the neutrophil in the vascular lumen. This observation seems to be promoted by the inflammation locally created via multiphoton activation of Rose Bengal.

A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy.

Ultrasound Localization Microscopy (ULM) can resolve the microvascular bed down to a few micrometers. To achieve such performance, microbubble contrast agents must perfuse the entire microvascular network. Microbubbles are then located individually and tracked over time to sample individual vessels, typically over hundreds of thousands of images. To overcome the fundamental limit of diffraction and achieve a dense reconstruction of the network, low microbubble concentrations must be used, which leads to acquisitions lasting several minutes. Conventional processing pipelines are currently unable to deal with interference from multiple nearby microbubbles, further reducing achievable concentrations. This work overcomes this problem by proposing a Deep Learning approach to recover dense vascular networks from ultrasound acquisitions with high microbubble concentrations. A realistic mouse brain microvascular network, segmented from 2-photon microscopy, was used to train a three-dimensional convolutional neural network (CNN) based on a V-net architecture. Ultrasound data sets from multiple microbubbles flowing through the microvascular network were simulated and used as ground truth to train the 3D CNN to track microbubbles. The 3D-CNN approach was validated in silico using a subset of the data and in vivo in a rat brain. In silico, the CNN reconstructed vascular networks with higher precision (81%) than a conventional ULM framework (70%). In vivo, the CNN could resolve micro vessels as small as 10 µ m with an improvement in resolution when compared against a conventional approach.

Laplacian Flow Dynamics on Geometric Graphs for Anatomical Modeling of Cerebrovascular Networks.

Generating computational anatomical models of cerebrovascular networks is vital for improving clinical practice and understanding brain oxygen transport. This is achieved by extracting graph-based representations based on pre-mapping of vascular structures. Recent graphing methods can provide smooth vessels trajectories and well-connected vascular topology. However, they require water-tight surface meshes as inputs. Furthermore, adding vessels radii information on their graph compartments restricts their alignment along vascular centerlines. Here, we propose a novel graphing scheme that works with relaxed input requirements and intrinsically captures vessel radii information. The proposed approach is based on deforming geometric graphs constructed within vascular boundaries. Under a laplacian optimization framework, we assign affinity weights on the initial geometry that drives its iterative contraction toward vessels centerlines. We present a mechanism to decimate graph structure at each run and a convergence criterion to stop the process. A refinement technique is then introduced to obtain final vascular models. Our implementation is available on https://github.com/Damseh/VascularGraph. We benchmarked our results with that obtained using other efficient and state-of-the-art graphing schemes, validating on both synthetic and real angiograms acquired with different imaging modalities. The experiments indicate that the proposed scheme produces the lowest geometric and topological error rates on various angiograms. Furthermore, it surpasses other techniques in providing representative models that capture all anatomical aspects of vascular structures.

Automated Analysis of Brain Microvasculature: From Segmentation to Anatomical Modeling.

Two-photon microscopy (TPM) can provide a detailed microscopic information of cerebrovascular structures. Extracting anatomical vascular models from TPM angiograms remains a tedious task due to image degeneration associated with TPM acquisitions and the complexity of microvascular networks. Here, we propose a fully automated pipeline capable of providing useful anatomical models of vascular structures captured with TPM. In the proposed method, we segment blood vessels using a fully convolutional neural network and employ the resulting binary labels to create an initial geometric graph enclosed within vessels boundaries. The initial geometry is then decimated and refined to form graphed curve skeletons that can retain both the vascular shape and its topology. We validate the proposed method on 3D realistic TPM angiographies and compare our results with that obtained through manual annotations.

Whole brain vascular imaging in a mouse model of Alzheimer's disease with two-photon microscopy.

Given known correlations between vascular health and cognitive impairment, the development of tools to image microvasculature in the whole brain could help investigate these correlations. We explore the feasibility of using an automated serial two-photon microscope to image fluorescent gelatin-filled whole rodent brains in three-dimensions (3-D) with the goal of carrying group studies. Vascular density (VD) was computed using automatic segmentation combined with coregistration techniques to build a group-level vascular metric in the whole brain. Focusing on the medial prefrontal cortex, cerebral cortex, the olfactory bulb, and the hippocampal formation, we compared the VD of three age groups (2-, 4.5-, and 8-months-old), for both wild type mice and a transgenic model (APP/PS1) with pathology resembling Alzheimer's disease (AD). We report a general loss of VD caused by the aging process with a small VD increase in the diseased animals in the somatomotor and somatosensory cortical regions and the olfactory bulb, partly supported by MRI perfusion data. This study supports previous observations that AD transgenic mice show a higher VD in specific regions compared with WT mice during the early and late stages of the disease (4.5 to 8 months), extending results to whole brain mapping.

Fully automated dual-resolution serial optical coherence tomography aimed at diffusion MRI validation in whole mouse brains.

An automated dual-resolution serial optical coherence tomography (2R-SOCT) scanner is developed. The serial histology system combines a low-resolution ( 25 µ m / voxel ) 3 × OCT with a high-resolution ( 1.5 µ m / voxel ) 40 × OCT to acquire whole mouse brains at low resolution and to target specific regions of interest (ROIs) at high resolution. The 40 × ROIs positions are selected either manually by the microscope operator or using an automated ROI positioning selection algorithm. Additionally, a multimodal and multiresolution registration pipeline is developed in order to align the 2R-SOCT data onto diffusion MRI (dMRI) data acquired in the same ex vivo mouse brains prior to automated histology. Using this imaging system, 3 whole mouse brains are imaged, and 250 high-resolution 40 × three-dimensional ROIs are acquired. The capability of this system to perform multimodal imaging studies is demonstrated by labeling the ROIs using a mouse brain atlas and by categorizing the ROIs based on their associated dMRI measures. This reveals a good correspondence of the tissue microstructure imaged by the high-resolution OCT with various dMRI measures such as fractional anisotropy, number of fiber orientations, apparent fiber density, orientation dispersion, and intracellular volume fraction.