Objective: Quantification of angiographic images with two-photon laser scanning fluorescence microscopy (2PLSM) relies on proper segmentation of the vascular images. However, the images contain inhomogeneities in the signal-to-noise ratio (SNR) arising from regional effects of light scattering and absorption. The present study developed a semiautomated quantification method for volume images of 2PLSM angiography by adjusting the binarization threshold according to local SNR along the vessel centerlines.Methods: A phantom model made with fluorescent microbeads was used to incorporate a region-dependent binarization threshold. Results:The recommended SNR for imaging was found to be 4.2-10.6 that provide the true size of imaged objects if the binarization threshold was fixed at 50% of SNR.However, angiographic images in the mouse cortex showed variable SNR up to 45 over the depths. To minimize the errors caused by variable SNR and a spatial extent of the imaged objects in an axial direction, the microvascular networks were threedimensionally reconstructed based on the cross-sectional diameters measured along the vessel centerline from the XY-plane images with adapted binarization threshold.The arterial volume was relatively constant over depths of 0-500 µm, and the capillary volume (1.7% relative to the scanned volume) showed the larger volumes than the artery (0.8%) and vein (0.6%). Conclusions:The present methods allow consistent segmentation of microvasculature by adapting the local inhomogeneity in the SNR, which will be useful for quantitative comparison of the microvascular networks, such as under disease conditions where SNR in the 2PLSM images varies over space and time.

Indocyanine green (ICG), a relatively nontoxic fluorescent compound, is known to bind to albumin after intravenous injection. 1 ICG is therefore used as a surrogate for blood plasma perfusion in human organs. ICG videoangiography (ICG-VA) is an imaging technique that is widely used during surgery because it provides real-time information on blood flow, the patency of blood vessels, and occlusion of aneurysms in a noninvasive and reliable manner. 2,3 Previous studies in patients undergoing neurosurgery quantified the time-intensity curves of ICG. The parameters quantified include maximum intensity, rise time, time to peak, time to half-maximal fluorescence, transit time, and blood flow index. 4 A growing body of research has shown that these values, measured from ICG-VA, characteristically reflect blood flow status in cerebrovascular diseases, such as subarachnoid hemorrhage, 5 stroke, 6 moyamoya disease, 7,8 traumatic brain injury, 9 atherosclerotic occlusive diseases, 10 and arteriovenous malformations. 11,12 Despite the widespread use of ICG-VA in neurosurgery, its use remains limited to relative measurements of the cerebral blood

Objective This study aimed to develop an automated image analysis method for segmentation and mapping of capillary flow dynamics captured using nailfold video capillaroscopy (NVC). Methods were applied to compare capillary flow structures and dynamics between young and middle‐aged healthy controls. Methods NVC images were obtained in a resting state, and a region of the vessel in the image was extracted using a conventional U‐Net neural network. The approximate length, diameter, and radius of the curvature were calculated automatically. Flow speed and its fluctuation over time were mapped using the Radon transform and frequency spectrum analysis from the kymograph image created along the vessel's centerline. Results The diameter of the curve segment (14.4 μm and 13.0 μm) and the interval of two straight segments (13.7 μm and 32.1 μm) of young and middle‐aged subjects, respectively, were significantly different. Faster flow was observed in older subjects (0.48 mm/s) than in younger subjects (0.26 mm/s). The power spectral analysis revealed a significant correlation between the high‐frequency power spectrum and the flow speed. Conclusions The present method allows a spatiotemporal characterization of capillary morphology and flow dynamics with NVC, allowing a wide application such as large‐scale health assessment.

Cerebral hemodynamics fluctuates spontaneously over broad frequency ranges. However, its spatiotemporal coherence of flow oscillations in cerebral microcirculation remains incompletely understood. The objective of this study was to characterize the spatiotemporal fluctuations of red blood cells (RBCs) and plasma flow in the rat cerebral microcirculation by simultaneously imaging their dynamic behaviors. Comparisons of changes in cross-section diameters between RBC and plasma flow showed dissociations in penetrating arterioles. The results indicate that vasomotion has the least effect on the lateral movement of circulating RBCs, resulting in variable changes in plasma layer thickness. Parenchymal capillaries exhibited slow fluctuations in RBC velocity (0.1 to 0.3 Hz), regardless of capillary diameter fluctuations (<0.1 Hz). Temporal fluctuations and the velocity of RBCs decreased significantly at divergent capillary bifurcations. The results indicate that a transit of RBCs generates flow resistance in the capillaries and that slow velocity fluctuations of the RBCs are subject to a number of bifurcations. In conclusion, the high-frequency oscillation of the blood flow is filtered at the bifurcation through the capillary networks. Therefore, a number of bifurcations in the cerebral microcirculation may contribute to the power of low-frequency oscillations.

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