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