In this study, we present a refined xf-irOCM system and post-processing pipeline for detailed investigation
of cerebral vessel structure and function. Our method uses deep learning for 3D segmentation of high-
resolution angiograms and accurately estimates flow velocities across the cerebral vasculature. Our graph-
based approach uniquely enables multiscale assessments, capturing data from intricate capillaries to broad
network relationships. Specifically, it aids in understanding vascular alterations in neurovascular
pathologies, such as stroke. Our approach will pave the way for future microvasculature studies, offering
promising avenues for further research into neurovascular diseases.
Comprehensive evaluation of microvascular function under normal and pathological conditions requires high-resolution three-dimensional microangiography capable of providing both morphological and functional information. Herein, we propose the stereovision Diffuse Optical Localization imaging (sDOLI) approach to attain transcranial volumetric brain microangiography through triangulation and stereo-matching of images collected with two short-wave infrared cameras. The spatio-temporal sparsity of flowing microparticles allows their precise localization while minimizing structural overlaps occurring in the dual-view projections. sDOLI is shown to preserve high spatial resolution which enables transcranial mapping of murine cortical microcirculation at capillary resolution while retrieving quantitative functional information across the entire mouse cortex.
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