Understanding the morphology and function of large-scale cerebrovascular networks is crucial for studying brain health and disease. However, reconciling the demands for imaging on a broad scale with the precision of high-resolution volumetric microscopy has been a persistent challenge. In this study, we introduce Bessel beam optical coherence microscopy with an extended focus to capture the full cortical vascular hierarchy in mice over 1000 × 1000 × 360 μm³ field-of-view at capillary level resolution. The post-processing pipeline leverages a supervised deep learning approach for precise 3D segmentation of high-resolution angiograms, hence permitting reliable examination of microvascular structures at multiple spatial scales. Coupled with high-sensitivity Doppler optical coherence tomography, our method enables the computation of both axial and transverse blood velocity components as well as vessel-specific blood flow direction, facilitating a detailed assessment of morpho-functional characteristics across all vessel dimensions. Through graph-based analysis, we deliver insights into vascular connectivity, all the way from individual capillaries to broader network interactions, a task traditionally challenging for in vivo studies. The new imaging and analysis framework extends the frontiers of research into cerebrovascular function and neurovascular pathologies.

了解大规模脑血管网络的形态和功能对于研究大脑健康和疾病至关重要。然而，协调对大范围成像的需求与高分辨率体积显微镜的精度一直是一项持续的挑战。在这项研究中，我们引入了贝塞尔光束光学相干显微镜，其扩展焦点在毛细管水平分辨率下捕获 1000 × 1000 × 360 μm³ 视野的小鼠的完整皮质血管层次结构。后处理管道利用有监督的深度学习方法对高分辨率血管造影进行精确的 3D 分割，从而允许在多个空间尺度上可靠地检查微血管结构。结合高灵敏度多普勒光学相干断层扫描，我们的方法能够计算轴向和横向血流分量以及血管特异性血流方向，从而有助于详细评估所有血管维度的形态功能特征。通过基于图形的分析，我们提供了对血管连接的见解，从单个毛细血管到更广泛的网络相互作用，这是传统上体内研究具有挑战性的任务。新的成像和分析框架将研究前沿扩展到脑血管功能和神经血管病理学。