Density masking is the de-facto quantitative imaging phenotype for emphysema that is widely used by the clinical community. Density masking defines the burden of emphysema by a fixed threshold, usually between -910 HU and -950 HU, that has been experimentally validated with histology. In this work, we formalized emphysema quantification by means of statistical inference. We show that a non-central Gamma is a good approximation for the local distribution of image intensities for normal and emphysema tissue. We then propose a test statistic in terms of the sample mean of a truncated non-central Gamma random variable. Our results show that this approach is well-suited for the detection of emphysema and superior to standard density masking. The statistical method was tested in a dataset of 1337 samples obtained from 9 different scanner models in subjects with COPD. Results showed an increase of 17% when compared to the density masking approach, and an overall accuracy of 94.09%.
Keywords: Emphysema quantification; non-central Gamma; statistical test; truncated random variable.