Consistent spring network representation of emphysematous lung from CT images
OA Version
Citation
Abstract
Emphysema is a progressive disease characterized by irreversible tissue
destruction and airspace enlargement, which manifest as low attenuation area (LAA) on
CT images. Previous studies have shown that inflammation, protease imbalance,
extracellular matrix remodeling and mechanical forces are collectively playing a role in
the progression of emphysema. Elastic spring network models have been applied to
investigate the pathogenesis of emphysema from the mechanical perspective. However,
all existing models include random removal of springs to mimic the initial locations of
LAA clusters from which emphysema progression is initiated. This approach is
generically lacking patient specificity of CT scans that precisely reflect the location of
LAA in an emphysematous lung. The aim of this work is to develop a novel approach
that provides an optimal spring network representation of emphysematous lungs based on
apparent density in CT images. The results suggest that the personalized elastic spring
network can be used to predict the propagation of structural destruction during
emphysema progression. Thus, our approach has the potential to predict disease
progression that should be verified by clinical data