Highly accurate, automated quantification of 2D/3D orientation for cerebrovasculature using window optimizing method

SIGNIFICANCE: Deep-imaging of cerebral vessels and accurate organizational characterization are vital to understanding the relationship between tissue structure and function. AIM: We aim at large-depth imaging of the mouse brain vessels based on aggregation-induced emission luminogens (AIEgens), and we create a new algorithm to characterize the spatial orientation adaptively with superior accuracy. APPROACH: Assisted by AIEgens with near-infrared-II excitation, three-photon fluorescence (3PF) images of large-depth cerebral blood vessels are captured. A window optimizing (WO) method is developed for highly accurate, automated 2D/3D orientation determination. The application of this system is demonstrated by establishing the orientational architecture of mouse cerebrovasculature down to the millimeter-level depth. RESULTS: The WO method is proved to have significantly higher accuracy in both 2D and 3D cases than the method with a fixed window size. Depth- and diameter-dependent orientation information is acquired based on in vivo 3PF imaging and the WO analysis of cerebral vessel images with a penetration depth of [Formula: see text] in mice. CONCLUSIONS: We built an imaging and analysis system for cerebrovasculature that is conducive to applications in neuroscience and clinical fields.