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Inter- and intra-rater agreement in assessing Interstitial Pulmonary Fibrosis (IPF) on CT scans is poor. Lack of approaches to understand the nuances of CT reconstruction parameters and their effect on patient scans hampers objective IPF quantification. In this paper, we propose an image analytic methodology to characterize the relationship between CT reconstruction parameters and IPF manifestation in scans. The raw data acquired from a patient was reconstructed with a combination of image thickness, interval and reconstruction kernels to derive 104 datasets. Quantitative and qualitative analyses were performed on the regional statistics computed in the neighborhood of each parenchymal voxel and for expert-chosen regions of interest (ROIs). The proposed approach could facilitate the selection of optimal CT reconstruction parameters to more robustly quantify IPF.

Recently we proposed spatio-pathological stratification of lungs from multiple subjects. This enabled a pulmonary disease landscape to objectively diagnose pathology, track progression and assess pharmacologic response within and across patients. Even though the approach based on unsupervised affinity propagation clustering of a symmetric pairwise dissimilarity metric showed strong statistical and clinical correlation, it did not address the possibility of candidates being potential outliers within a cluster and consequently being triaged to suboptimal personalized care. In this paper, we address this limitation through the use of an asymmetric dissimilarity metric and a density-based outlier detection technique to identify the natural outliers within the individual clusters. In a database of 370 datasets, 28 outliers were detected among 20 clinically correlated clusters. The proposed quantitative analytics could facilitate an optimized landscape wherein every patient is triaged through the most appropriate individualized pulmonary care.