Recent advancements in multiphoton imaging and vascular reconstruction algorithms have increased the amount of data on cerebrovascular circulation for statistical analysis and hemodynamic simulations. Experimental observations offer fundamental insights into capillary network topology but mainly within a narrow field of view typically spanning a small fraction of the cortical surface (less than 2%). In contrast, larger-resolution imaging modalities, such as computed tomography (CT) or magnetic resonance imaging (MRI), have whole-brain coverage but capture only larger blood vessels, overlooking the microscopic capillary bed. To integrate data acquired at multiple length scales with different neuroimaging modalities and to reconcile brain-wide macroscale information with microscale multiphoton data, we developed a method for synthesizing hemodynamically equivalent vascular networks for the entire cerebral circulation. This computational approach is intended to aid in the quantification of patterns of cerebral blood flow and metabolism for the entire brain. In part I, we described the mathematical framework for image-guided generation of synthetic vascular networks covering the large cerebral arteries from the circle of Willis through the pial surface network leading back to the venous sinuses. Here in part II, we introduce novel procedures for creating microcirculatory closure that mimics a realistic capillary bed. We demonstrate our capability to synthesize synthetic vascular networks whose morphometrics match empirical network graphs from three independent state-of-the-art imaging laboratories using different image acquisition and reconstruction protocols. We also successfully synthesized twelve vascular networks of a complete mouse brain hemisphere suitable for performing whole-brain blood flow simulations. Synthetic arterial and venous networks with microvascular closure allow whole-brain hemodynamic predictions. Simulations across all length scales will potentially illuminate organ-wide supply and metabolic functions that are inaccessible to models reconstructed from image data with limited spatial coverage.

中文翻译：

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整个小鼠大脑皮质循环的数学合成——第二部分：微循环闭合

多光子成像和血管重建算法的最新进展增加了用于统计分析和血流动力学模拟的脑血管循环数据量。实验观察提供了对毛细血管网络拓扑结构的基本见解，但主要是在一个狭窄的视野内，通常跨越一小部分皮质表面（小于 2%）。相比之下，更高分辨率的成像方式，如计算机断层扫描 (CT) 或磁共振成像 (MRI)，具有全脑覆盖，但仅捕获较大的血管，俯瞰微观毛细血管床。将在多个长度尺度上采集的数据与不同的神经成像模式相结合，并将全脑宏观尺度信息与微观尺度多光子数据相协调，我们开发了一种为整个脑循环合成血流动力学等效的血管网络的方法。这种计算方法旨在帮助量化整个大脑的脑血流和新陈代谢模式。在第一部分中，我们描述了图像引导生成合成血管网络的数学框架，该网络覆盖从 Willis 环到通向静脉窦的软脑膜表面网络的大脑动脉。在第二部分中，我们介绍了创建新程序 我们描述了用于图像引导生成合成血管网络的数学框架，该网络覆盖从 Willis 环到通向静脉窦的软脑膜表面网络的大脑动脉。在第二部分中，我们介绍了创建新程序 我们描述了用于图像引导生成合成血管网络的数学框架，该网络覆盖从 Willis 环到通向静脉窦的软脑膜表面网络的大脑动脉。在第二部分中，我们介绍了创建新程序模拟真实毛细血管床的微循环闭合。我们展示了我们合成合成血管网络的能力，其形态计量学与来自三个独立的最先进的成像实验室使用不同的图像采集和重建协议的经验网络图相匹配。我们还成功合成了适合进行全脑血流模拟的完整小鼠大脑半球的十二个血管网络。具有微血管闭合的合成动脉和静脉网络允许全脑血流动力学预测。跨所有长度尺度的模拟将潜在地阐明器官范围的供应和代谢功能，这些功能对于从空间覆盖有限的图像数据重建的模型来说是无法访问的。