Physics-Based Computational Modeling of Mechanics and Perfusion in COVID-19 Infected Lungs
This item will be available on: 2024-07-01
SARS-CoV-2 is the newest virus to lead to severe respiratory illness and reach global pandemic status in its spread. Histopathological studies as well as global lung dynamics of the disease state of COVID-19 resemble acute respiratory distress syndrome but with some marked differences that have yet to be fully understood. Studying COVID-19 infected lung mechanics and perfusion provides a unique opportunity to advance our foundational knowledge of lung dynamics in a disease state. Lung mechanics emerge from the complex structure of the microscale lung; hence, understanding lung micro- and meso-scale mechanical properties is required to shed light on the macroscale mechanical properties of the whole lung. Current limitations of in vivo and in vitro methods can make it difficult to understand the effects of viral damage on dynamic function of the microscale lung. Thus, computational modeling of microscale lung mechanics in disease may provide new insights into whole lung dynamics. To this end, this thesis provides a review on the current knowledge on COVID-19 lung mechanics and develops physics-based computer models of the healthy and COVID-19 affected micro and meso-scale lung in addition to multi-scale perfusion-ventilation models. A fully resolved model of the mesoscale lung is used to reconcile the range of reported mechanical properties for healthy lung mechanics across the meso- and microscale. Furthermore, a reduced dimensional approach is used to model the acinus and account for the ventilation, perfusion, and diffusion processes that contribute to gas exchange function in the lung. The acinar model is utilized in a multiscale lung model to gain insight into COVID-19 lung mechanics in a patient-specific approach. These models provide informative tools for lung mechanics researchers and are a foundational step towards understanding and modeling the complex microscale lung damage that produces the spectrum of COVID-19 disease symptoms.
Dimbath, Elizabeth. (July 2022). Physics-Based Computational Modeling of Mechanics and Perfusion in COVID-19 Infected Lungs (Master's Thesis, East Carolina University). Retrieved from the Scholarship. (http://hdl.handle.net/10342/11137.)
Dimbath, Elizabeth. Physics-Based Computational Modeling of Mechanics and Perfusion in COVID-19 Infected Lungs. Master's Thesis. East Carolina University, July 2022. The Scholarship. http://hdl.handle.net/10342/11137. September 28, 2023.
Dimbath, Elizabeth, “Physics-Based Computational Modeling of Mechanics and Perfusion in COVID-19 Infected Lungs” (Master's Thesis., East Carolina University, July 2022).
Dimbath, Elizabeth. Physics-Based Computational Modeling of Mechanics and Perfusion in COVID-19 Infected Lungs [Master's Thesis]. Greenville, NC: East Carolina University; July 2022.
East Carolina University