Derivation of a test statistic for emphysema quantification

• Published in: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) Vol. 2016; pp. 1269 - 1273
• Main Authors: Vegas-Sanchez-Ferrero, Gonzalo, Washko, George, Rahaghi, FarbodN, Ledesma-Carbayo, Maria J., Estepar, R. San Jose
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01-04-2016
• Subjects: Attenuation; Computed tomography; Density; Emphysema; Emphysema quantification; Gaussian distribution; Inference; Lungs; Masking; non-central Gamma; Random variables; Samples; Statistical analysis; Statistical methods; statistical test; Statistical tests; Testing; truncated random variable; non-central Gamma; statistical test; truncated random variable; Emphysema quantification
• ISSN: 1945-7928; 1945-8452
• DOI: 10.1109/ISBI.2016.7493498

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%.