Detection and monitoring of patients with pulmonary hypertension, defined as a mean blood pressure in the main pulmonary artery above 25 mmHg, requires a combination of imaging and hemodynamic measurements. This study demonstrates how to combine imaging data from microcomputed tomography images with hemodynamic pressure and flow waveforms from control and hypertensive mice. Specific attention is devoted to developing a tool that processes computed tomography images, generating subject-specific arterial networks in which one-dimensional fluid dynamics modeling is used to predict blood pressure and flow. Each arterial network is modeled as a directed graph representing vessels along the principal pathway to ensure perfusion of all lobes. The one-dimensional model couples these networks with structured tree boundary conditions representing the small arteries and arterioles. Fluid dynamics equations are solved in this network and compared to measurements of pressure in the main pulmonary artery. Analysis of microcomputed tomography images reveals that the branching ratio is the same in the control and hypertensive animals, but that the vessel length-to-radius ratio is significantly lower in the hypertensive animals. Fluid dynamics predictions show that in addition to changed network geometry, vessel stiffness is higher in the hypertensive animal models than in the control models.

检测和监测肺动脉高压患者（定义为主肺动脉平均血压高于 25 mmHg）需要结合影像学和血流动力学测量。这项研究展示了如何将来自微计算机断层扫描图像的成像数据与来自对照和高血压小鼠的血流动力学压力和流量波形相结合。特别关注开发一种处理计算机断层扫描图像的工具，生成特定主题的动脉网络，其中使用一维流体动力学建模来预测血压和流量。每个动脉网络都被建模为一个有向图，代表沿着主要通路的血管，以确保所有叶的灌注。一维模型将这些网络与代表小动脉和小动脉的结构化树边界条件相结合。在该网络中求解流体动力学方程，并与主肺动脉中的压力测量值进行比较。显微计算机断层扫描图像的分析表明，对照组和高血压动物的分支比相同，但高血压动物的血管长度与半径比明显较低。流体动力学预测表明，除了改变网络几何形状外，高血压动物模型中的血管刚度高于对照模型。显微计算机断层扫描图像的分析表明，对照组和高血压动物的分支比相同，但高血压动物的血管长度与半径比明显较低。流体动力学预测表明，除了改变网络几何形状外，高血压动物模型中的血管刚度高于对照模型。显微计算机断层扫描图像的分析表明，对照组和高血压动物的分支比相同，但高血压动物的血管长度与半径比明显较低。流体动力学预测表明，除了改变网络几何形状外，高血压动物模型中的血管刚度高于对照模型。