###### Abstract
In this paper, we describe the process of identifying lung abnormalities in tissue density maps from MRI data obtained in a small sample of CF and healthy control subjects, the identification of key summary statistics that spatially resolve regional and global disease in CF when compared to controls, and define our model that categorizes the severity and spatial pattern of disease in CF. An MR imaging sequence specifically designed for rapid quantitative lung density assessment has been developed at the University of California, San Diego (UCSD) that is capable of measuring lung water density on a single voxel basis within a nine-second breath-hold. The measure of lung water density is unitless and is presented as fractional lung density (FLD) with a range of values from 0 (air) to 1 (100% fluid). This MR technique has been shown to be a reliable and repeatable technique to measure lung water density in healthy subjects [22] and has been validated in an ex vivo animal study [23]. It has also been designed so that it can be installed and used on any clinical MR imaging scanner without investing in additional hardware or personnel. This technique can reliably evaluate a change of \(\pm\)0.01 in lung density (\(\pm\)0.006 cm\({}^{3}\) of fluid) per MR image voxel (\(\pm\)0.6 cm\({}^{3}\) of fluid). Thus, any observed change in lung density between lung volumes identifies very small changes in regional lung water content. We acknowledge that the MR imaging technique is not capable of approaching the resolution of CT (\(\sim\)0.001 cm\({}^{3}\)), but our technique is very sensitive in detecting very small changes in lung water content. Data acquired with this MR imaging sequence has been obtained for 13 CF subjects across mild-to-severe disease severity and age- and sex-matched healthy control subjects. In this study, we have developed a digital image analysis technique to import MR lung density images and identify excess fluid indicative of lung abnormalities. This is done by segment ing lung regions with an abnormally high tissue density when compared to healthy control subjects. There are five major classes of lung segmentation methods: (i) thresholding-based, (ii) region-based, (iii) shape-based, (iv) neighboring anatomy-guided, and (v) machine learning-based methods [24]. In this study, we use a thresholding-based method to segment the excess fluid in the lungs by determining the normal fluid content present within the control group population. Thresholding-based methods are fast and the least expensive computationally [24]. Additionally, thresholding-based methods are well suited for the identification and segmentation of well-defined normal structures [24], although they often fail to categorize pathologic features. However, by comparing segmented images of CF subjects with healthy controls, we were able to obtain well-defined features of excess fluid for accurate segmentation. In culmination, our approach characterizes the spatial presentations of excess fluid in each CF subject and healthy control subject based on the location, size, and density of excess fluid present in different regions of their lungs. The percent area of excess fluid, referred to as the spatial distribution, is determined for the total lung and different regions of interest. We found that spatial distribution is a key factor in identifying the differences in spatial presentation between CF and control subjects. We have developed metrics based on spatial distribution to capture differences in excess fluid regionally and globally. These metrics were used to define the spatial presentation of each lung in one of three groups: (i) generalized if the fluid was sparse, (ii) localized diffuse if at least one locality of fluid was present with a dense distribution of fluid elsewhere, and (iii) localized if at least one locality of fluid was present with a sparse distribution elsewhere.