RAPID: A Computational Model for Multiscale Investigation of Regional Lung Dynamics

这项快速反应研究（Rapid）拨款将基于住院COVID-19患者的成像数据，为呼吸和机械通气的机械方面提供新的基本见解。虽然经常使用机械通风，但我们对其工作原理的科学理解很少。一个由工程师和医生组成的多学科团队将共同努力，更好地了解肺部对机械通气的反应。这项工作将使用COVID-19患者的成像数据来创建受感染和发炎肺部的详细计算机模型。这些患者特异性计算机模拟将使在健康和患病状态下的肺功能的机制理解。然后，本研究中创建的虚拟肺将用于模拟机械通气下COVID-19感染肺的数字双胞胎。因此，我们对肺部疾病的机械方面和对通气的身体反应的基本知识将得到提高。此外，在这个项目中开发的计算机模型将被用作多个大学课程的教学材料。这将为来自农村社区的低收入和第一代大学生介绍尖端的建模和仿真技术。这种经历会对这些学生对基础研究的兴趣和职业目标产生深远的影响。肺部的通气和呼吸事件跨越多个长度和时间尺度。此外，这些事件涉及多种物理现象，包括肺实质的变形和运动，空气、气道壁和肺泡之间的流-固相互作用，最后与流经肺泡毛细血管的血液进行扩散和气体交换。虽然在宏观尺度上对肺力学的理解已经取得了适度的进展，但我们对肺泡（肺的最小功能单位）水平上肺的微尺度和中尺度力学的认识仍处于初级阶段。我们提出的基于COVID-19受试者CT成像的疾病状态下肺的多尺度和多物理场计算建模的研究，有可能推进我们肺动力学的基础和机制知识。通过针对患者的多物理场和多尺度建模来理解和探索COVID-19患者肺泡弥散减少的机制，可以丰富我们的知识基础，并为力学在肺部炎症中的作用提供新的见解。此外，使用急性炎症模型和CT数据进行肺的多尺度模拟可以告知和教育炎症引起的肺变化和肺泡-毛细血管力学的科学。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Rapid Response Research (RAPID) grant will offer new fundamental insights into the mechanical aspects of breathing and mechanical ventilation based on imaging data from hospitalized COVID-19 patients. While mechanical ventilation is often used, our scientific understanding of how it works is sparse. A multidisciplinary team of engineers and physicians will work together to better understand how the lungs respond to mechanical ventilation. This work will use imaging data from COVID-19 patients to create detailed computer models of the infected and inflamed lungs. These patient-specific computer simulations will enable mechanistic understanding of lung function in healthy and diseased states. The virtual lungs created in this study will then be used to simulate a digital twin of the COVID-19 infected lungs on mechanical ventilation. Thus, our fundamental knowledge of the mechanical aspects of lung disease and the physical response to ventilation will be advanced. Furthermore, the computer models developed in this project will be used as instructional material for multiple university courses. This will introduce low-income and first-generation college students from rural communities to cutting-edge modeling and simulation techniques. This experience can have a profound impact on these students' interest in foundational research and career goals. Ventilation and respiration events in the lungs span multiple length and time scales. Furthermore, these events involve multiple physical phenomena, including deformation and motion of the lung parenchyme, fluid-solid interaction between air, airway walls and the alveoli, and finally diffusion and gas exchange with the blood flowing through the alveolar capillaries. While modest progress has been made towards understanding the mechanics of the lung at the macroscale, our knowledge of the microscale and mesoscale mechanics of the lung at the level of alveoli, the smallest functional unit of the lung, remains rudimentary. Our proposed research on multiscale and multiphysics computational modeling of the lung in a disease state, based on CT imaging in COVID-19 subjects, offers the potential to advance our fundamental and mechanistic knowledge of lung dynamics. Understanding and exploring the mechanisms of reduced alveolar diffusion in COVID-19 patients through patient-specific multi-physics and multiscale modeling can advance our knowledge base and provide new insights into the role of mechanics in lung inflammation. Furthermore, multiscale simulation of the lung using an acute inflammation model and CT data can inform and educate about the science of inflammation-induced changes in the lungs and the alveolar-capillary mechanics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.