New Image Analysis Technique for Quantitative Longitudinal Assessment of Lung Pathology on CT in Infected Rhesus Macaques

Abstract

This paper describes a novel method of quantitative assessment of lung pathology derived from chest computed tomography (CT) scans in infected animal models, namely rhesus macaques. Tracking the extent of lung pathology is essential in the understanding of the natural history of infectious diseases and can be eventually used to predict prognosis and monitor response to preventative (vaccines) or therapeutic interventions. Our technique utilizes the histogram of voxel Hounsfield units (HU) within the segmented lung to track the percent change in "hyper dense volume" as a marker of disease over time. This method is not as susceptible to variability in lung inflation from breath hold techniques during the scanning process as are other techniques. Our quantitative lung pathology estimates using this technique correlated well with qualitative interpretation of lung pathology performed by a radiologist.