Remodeling of Pulmonary Cardiovascular Networks in the Presence of Hypertension

研究小组将开发肺动脉高压研究的数学和计算模型。这是一种罕见但进展迅速的心血管疾病，死亡率高。肺动脉高压既包括血压升高，也包括肺循环内血管壁硬度和厚度的改变。这些问题随着疾病的严重程度而恶化。诊断疾病类别与肺循环网络的不同部分相关，但确定疾病的起始位置具有挑战性。将结合合作者提供的实验数据开发模型。这些数据将通过对血管血流和血管网络结构的非侵入性成像，以及通过导管置入对小鼠和人类肺血管的血压进行侵入性测量获得。心脏腔、大血管、小血管网络及其相互作用的影响将基于流体力学、固体力学、网络分析、逆问题和参数估计的建模和方法方法来捕获。所提出的肺心血管模型有潜力被纳入使用非侵入性测量来预测压力的诊断方案，以减少侵入性随访程序的数量，并作为识别与疾病诊断类别和疾病进展程度相关的特征的重要组成部分。整个项目还为博士后、研究生和本科生在数据驱动的生物医学研究方面提供了各种综合培训机会。系统级，一维的肺循环数学和计算流体动力学模型将开发。这些模型将包括右心室、左心房、大、小肺动脉和静脉，并考虑到肺动脉高压引起的系统成分的改变。将设计一个基于生理的动脉和静脉壁模型，该模型考虑了胶原蛋白和弹性蛋白的含量，也可以捕捉肺高压反应中壁成分的重塑。该模型将植根于非线性弹性理论，并将产生一个更稳健的压力-面积关系，可以集成到流体动力学模型中，并线性化以促进从大容器到小容器的过渡。第二种基于生理学的右心室模型结合了简单弹性函数和单纤维模型的思想。该模型将能够预测弹性作为右心室厚度的函数。分析将集中在肺循环，但也将考虑在综合闭环模型中建模体循环的影响。在这两个目标中开发的模型将用于模拟和识别流量和压力波形，预测与疾病进展相关的特征，并通过敏感性分析和参数估计，使模型具有患者特异性。有了这些创新，将有可能使用一维系统级模型结合肺动脉血压，通过无创测量血流来评估与肺动脉高压相关的疾病进展，同时也减少了有创测量的次数。

The research team will develop mathematical and computational models for the study of pulmonary hypertension. This is a rare but rapidly progressing cardiovascular disease with a high mortality rate. Pulmonary hypertension involves both elevated blood pressure and changes in vessel wall stiffness, and thickness within the pulmonary circulation. These issues worsen with the severity of the disease. Diagnostic disease categories are associated with different parts of the pulmonary circulation network, yet pinpointing locations where the disease initiates is challenging. Models will be developed in conjunction with the use of experimental data provided by collaborators. This data will be obtained from non-invasive imaging of vascular blood flow and vessel network structure, and from invasive measurements of blood pressure in pulmonary vessels in mice and humans obtained via catheterization. Effects of the heart chambers, large vessels, small vessel networks and their interactions will be captured based on modeling and methodological approaches from fluid mechanics, solid mechanics, network analysis, inverse problems and parameter estimation. The proposed pulmonary cardiovascular model has potential to be incorporated into diagnostic protocols predicting pressure using non-invasive measurements to reduce the number of the invasive follow-up procedures, and to serve as a vital component for identifying signatures associated with disease diagnostic categories and the degree of disease progression. The overall project also provides a variety of opportunities for integrated training of a postdoc, and graduate and undergraduate students, in data-driven biomedical research.System-level, one-dimensional mathematical and computational fluid dynamics models of the pulmonary circulation will be developed. These models will include the right ventricle, left atrium, the large and small pulmonary arteries and veins, and account for alterations in the system components due to pulmonary hypertension. A physiologically based arterial and venous wall model that accounts for collagen and elastin content and that can also capture remodeling of wall constituents in response to pulmonary hypertension will be designed. This model will be rooted in nonlinear elasticity theory and will yield a more robust pressure-area relation that can be integrated within the fluid dynamics model, and linearized to facilitate the transition from large to small vessels. A second physiologically based right ventricle model combining ideas from simple elastance functions and single-fiber models will also be provided. This model will enable prediction of elastance as a function of right ventricle thickness. The analysis will focus on the pulmonary circulation, but impacts on modeling systemic circulation in a comprehensive closed loop model will also be considered. The models developed in these two aims will be used to simulate and identify flow and pressure waveforms that predict features associated with disease progression and, via sensitivity analysis and parameter estimation, to render the model patient-specific. With these innovations it will be possible to use the one dimensional system level model combined with pulmonary arterial blood pressure from non-invasive measurements of flow to assess disease progression associated with pulmonary hypertension while also reducing the number of invasive measurements.