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We propose a novel framework for exploring patterns of respiratory pathophysiology from paired breath-hold CT scans. This is designed to enable analysis of large datasets with the view of determining relationships between functional measures, disease state and the likelihood of disease progression. The framework is based on the local distribution of image features at various anatomical scales. Principal Component Analysis is used to visualise and quantify the multi-scale anatomical variation of features, whilst the distribution subspace can be exploited within a classification setting. This framework enables hypothesis testing related to the different phenotypes implicated in Chronic Obstructive Pulmonary Disease (COPD). We illustrate the potential of our method on initial results from a subset of patients from the COPDGene study, who are exacerbation susceptible and non-susceptible.

Multi-scale Analysis of Imaging Features and Its Use in the Study of COPD Exacerbation Susceptible Phenotypes