Predicting changes in lung structure and function during emphysema progression through network modeling methods

Emphysema is a type of Chronic Obstructive Pulmonary Disease (COPD) characterized by breathing difficulties due to airflow obstruction, and results in structural and functional changes of the lungs. Structural changes include alveolar wall destruction and the formation of enlarged alveoli, or bullae, which appear as low attenuation areas in the CT image of emphysematous lungs. Functional changes include increased lung compliance and decreased bulk modulus in emphysematous lungs. Previous mathematical and computational models have attempted to explain either general lung structure or function, but have not linked the two to explore patient-specific lung mechanics. We propose that we can link the structure and function by creating CT-based spring network models of the lung parenchyma and manipulating these networks to predict the regional tissue stiffness and global pressure-volume relationship of the lung during disease progression. The goal of this thesis is to predict these patient-specific changes during emphysema progression by approximating the lung tissue stiffness distribution from CT densities and predicting parenchymal destruction over time from high-strain regions of a non-linear elastic spring network representing lung tissue. First, we used simple spring network models to determine the appropriate non-linear spring force-extension equation to implement into the full lung network. We then mapped a spring network onto a CT image to create a lung network, applied the non-linear force-extension equation to the network springs, and developed a lung deflation model to capture the quasi-static pressure-volume curve of the lung. Finally, we reduced the stiffness of high-strain regions of the lung network and deflated the model to predict the loss of tissue elastance and the reduced bulk modulus over time. Our method shows evidence of a reduced bulk modulus and similar tissue destruction between predicted and actual lung networks, but further development and testing are necessary to create more accurate prediction network models