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.
