Knowledge-based quantification of pericardial fat in non-contrast CT data

Recent studies show that pericardial fat is associated with vascular calcification and cardiovascular risk. The fat is imaged with Computed Tomography (CT) as part of coronary calcium scoring but it is not included in routine clinical analysis due to the lack of automatic tools for fat quantification. Previous attempts to create such an automated tool have the limitations of either assuming a preset threshold or a Gaussian distribution for fat. In order to overcome these limitations, we present a novel approach using a classification-based method to discriminate fat from other tissues. The classifier is constructed from three binary SVM classifiers trained separately for multiple tissues (fat, muscle/blood and calcium), and a specific code is assigned to each tissue type based on the number of classifiers. The decisions of these binary classifiers are combined and compared with previously determined codes using a minimum Hamming decoding distance to identify fat. We also present an improved method for detection of a compact region-of-interest around the heart to reduce the number of false positives due to neighboring organs. The proposed method UH-PFAT attained a maximum overlap of 87%, and an average overlap of 76% with expert annotations when tested on unseen data from 36 subjects. Our method can be improved by identifying additional discriminative features for fat and muscle/blood separation, or by using more advanced classification approaches such as cascaded classifiers to reduce the number of false detections.