Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study

PURPOSE: To explore the effects of computed tomography (CT) slicethickness and reconstruction algorithm on quantification of image features to characterizetumors using a chest phantom. MATERIALS AND METHODS: Twenty-two phantomlesions of known sizes (10 and 20 mm), shapes (spherical, elliptical, lobulated, andspiculated), and densities [-630, -10, and +100 Hounsfield Unit (HU)] were inserted intoan anthropomorphic thorax phantom and scanned three times with relocations. The raw datawere reconstructed using six imaging settings, i.e., a combination of three slicethicknesses of 1.25, 2.5, and 5 mm and two reconstruction kernels of lung and standard.Lesions were segmented and 14 image features representing lesion size, shape, and texturewere calculated. Differences in the measured image features due to slice thickness andreconstruction algorithm were compared using linear regression method by adjusting threeconfounding variables (size, density, and shape). RESULTS: All 14features were significantly different between 1.25 and 5 mm slice images. The 1.25 and 2.5mm slice thicknesses were better than 5 mm for volume, density mean, density SD gray-levelco-occurrence matrix (GLCM) energy and homogeneity. As for the reconstruction algorithm,there was no significant difference in uni-dimension, volume, shape index 9, andcompactness. Lung reconstruction was better for density mean, whereas standardreconstruction was better for density SD. CONCLUSIONS: CT slice thicknessand reconstruction algorithm can significantly affect the quantification of imagefeatures. Thinner (1.25 and 2.5 mm) and thicker (5 mm) slice images should not be usedinterchangeably. Sharper and smoother reconstructions significantly affect thedensity-based features.