Quantifying Emphysema From Chest Computed Tomography Scans Using Integral Geometry Descriptors: Improved Performance Over Density Measures In Low Dose Scans

RATIONALE: To gain insight into the underlying pathways of emphysema and monitor the effect of treatment, methods to quantify emphysema from low dose chest computed tomography (CT) scans are of crucial importance. Currently, emphysema is often quantified in CT scans by densitometric measures (e.g. relative area under -950 HU (RA950)), which are known to be affected by inspiration level of the patient and scanner parameters. Especially in low-dose CT scans, image noise can lead to an increase in RA950. The purpose of this study is to compare a method for quantifying emphysema from low dose chest CT scans using texture analysis based on integral geometry descriptors to RA950. METHODS: Standardized pre-treatment CT scans from 13 subjects enrolled in clinical trials for emphysema treatment were taken from an anonymized research database. CT imaging of the thorax was performed at suspended full inspiration with at most 1.25 mm slice thickness, 0.6 mm slice spacing at 120 KV and 25-40 mAs. An automatic method to classify each voxel into emphysema (E) or non-emphysema (NE) was developed based on Minkowski Functionals and rotationally invariant Gaussian features. A combination of nine features was used to train a k-nearest neighbor classifier which assigns to each voxel in a test scan a label E or NE. To evaluate the automatic method, 18 regions of interest (ROI) were randomly placed in the lungs in each scan, equally divided over 6 different zones in the lungs. An observer study was performed in which expert chest radiologists annotated each ROI as being either NE or E. The texture analysis method was applied to the ROIs in a leave-one-patient-out fashion. An emphysema score (ES) was defined as the percentage of voxels in the ROI classified as emphysematous. RA950 was also calculated for each ROI. For both methods, an ES > 5% was defined to be emphysematous. The output of the texture analysis method and RA950 were compared to the visual assessment using confusion matrices. RESULTS: The confusion matrix comparing the proposed method and RA950 to the visual annotation is shown in Table 1. The standard RA950 measure has an accuracy for classifying emphysema vs. non-emphysema of 67%, the proposed method has an accuracy of 89%. Figure 1 illustrates the effect of image noise on both methods. CONCLUSION: The proposed method is able to quantify emphysema with a better accuracy than the standard RA950 method in low dose chest CT scans.