Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study |
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Authors: | Binsheng Zhao Yongqiang Tan Wei Yann Tsai Lawrence H Schwartz Lin Lu |
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Affiliation: | 2. Mailman School of Public Health, Columbia University Medical Center, New York, NY |
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Abstract: | PURPOSE: To explore the effects of computed tomography (CT) slice
thickness and reconstruction algorithm on quantification of image features to characterize
tumors using a chest phantom. MATERIALS AND METHODS: Twenty-two phantom
lesions of known sizes (10 and 20 mm), shapes (spherical, elliptical, lobulated, and
spiculated), and densities -630, -10, and +100 Hounsfield Unit (HU)] were inserted into
an anthropomorphic thorax phantom and scanned three times with relocations. The raw data
were reconstructed using six imaging settings, i.e., a combination of three slice
thicknesses 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 texture
were calculated. Differences in the measured image features due to slice thickness and
reconstruction algorithm were compared using linear regression method by adjusting three
confounding variables (size, density, and shape). RESULTS: All 14
features were significantly different between 1.25 and 5 mm slice images. The 1.25 and 2.5
mm slice thicknesses were better than 5 mm for volume, density mean, density SD gray-level
co-occurrence matrix (GLCM) energy and homogeneity. As for the reconstruction algorithm,
there was no significant difference in uni-dimension, volume, shape index 9, and
compactness. Lung reconstruction was better for density mean, whereas standard
reconstruction was better for density SD. CONCLUSIONS: CT slice thickness
and reconstruction algorithm can significantly affect the quantification of image
features. Thinner (1.25 and 2.5 mm) and thicker (5 mm) slice images should not be used
interchangeably. Sharper and smoother reconstructions significantly affect the
density-based features. |
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