COPD is a major cause of morbidity and mortality. Despite declines in smoking, mortality from COPD continues to increase and is now the 3rd leading cause of death in the US. COPD is a heterogeneous disease, characterized in part by airway remodeling and emphysematous destruction of parenchyma. Emphysema is a key COPD-related phenotype with a genetic and epigenetic component. Emphysema progression is, however, poorly understood and current tools available to measure emphysema progression are limited. Longitudinal studies have confirmed that the presence and severity of emphysema vary greatly in patients with COPD and the progression analysis of emphysema varies substantially among patients due to confounding factors involved in the analysis of lung injury progression with CT scans. The immediate consequence is the need of too large sample size for application in clinical trials due to the reduction of statistical power. The main confounding factors affecting the analysis of emphysema progression are the impairments of calibrations (inter-device variability), the biological changes in the scanned subject (increase of size and volume), variability in the acquisition (dose, reconstruction methods), and intra-subject variability due to the non-homogeneous behavior of noise. This project will take full advantage of the most recent developments in image-driven statistical characterization of tissues to reduce the harmful effects of the main confounding factors affecting the analysis of emphysema progression. The standardization of CT scans in a statistical framework will enable the definition of powerful statistical test to assess the extent and activity of lung abnormalities, the test will be statistically robust to noise and unpaired calibrations. Additionally, it will provide a map of p-values. Finally, we will define different progression endpoints based densitometry of the standardized CT scans that will be validated performing association with clinical outcomes, inflammation biomarkers, and genetic variants. Our preliminary data obtained with our recently published methods show promising results. We show that we can effectively obtain robust estimators to noise that also preserve the structural information of parenchyma. Additionally, our noise-stabilization methods transform the non-homogenous noise a homogeneous noise that can be efficiently characterized. Our tissue characterization in CT images also has proved its suitability to correct he inter-device impairments common in longitudinal studies. Together, the research proposed in the aims of this award will take full advantage of the comprehensive dataset available through the COPDGene study. The execution of the aims in this proposal will be possible through active collaboration with Dr. Raul San Jose Estepar, Ph.D. as the mentor; and an outstanding Advisory Committee including renowned leaders in the fields of medical image analysis, translational research, quantitative imaging in COPD, the genetic epidemiology of COPD.