Rationale: Chronic obstructive pulmonary disease (COPD) is characterized by airflow limitation, caused by emphysema and small airways disease (SAD). Computed tomography (CT) coupled with image analysis enables the quantification of these abnormalities; however, the optimum method for doing so has not been determined.Objectives: This study aims to compare two CT quantitative analysis techniques, disease probability measure (DPM) and parametric response mapping (PRM), and assess their relationship with specific physiological measures of SAD.Methods: Subjects with mild to moderate COPD, never smokers, and healthy ex-smokers were recruited. Each had airway oscillometry and multiple-breath nitrogen washout, measuring peripheral airway resistance, peripheral airway reactance, and acinar airway inhomogeneity. Subjects also had an inspiratory and expiratory chest CT, with DPM and PRM analysis performed by coregistering images and classifying each voxel as normal, emphysema, or nonemphysematous gas trapping related to SAD.Results: Thirty-eight subjects with COPD, 18 never smokers, and 23 healthy ex-smokers were recruited. There were strong associations between DPM and PRM analysis when measuring gas trapping (ρ = 0.87; P < 0.001) and emphysema (ρ = 0.99; P < 0.001). DPM assigned significantly more voxels as emphysema and gas trapped than PRM (P < 0.001). Both techniques showed significantly greater emphysema and gas trapping in subjects with COPD than in never smokers and ex-smokers (P < 0.001). All CT measures had significant associations with peripheral airway resistance and reactance, with disease probability measure of nonemphysematous gas trapping related to SAD having the strongest independent association with peripheral airway resistance (β = 0.42; P = 0.001) and peripheral airway reactance (β = 0.41; P = 0.001). Emphysema measures had the strongest associations with acinar airway inhomogeneity (β = 0.35-0.38).Conclusions: These results provide further validation for the use of DPM/PRM analysis in COPD by demonstrating significant relationships with specific physiological measures of SAD.

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