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


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.
Department of Radiology, Columbia University Medical Center, New York, NY.
Binsheng
B
Zhao
Department of Radiology, Columbia University Medical Center, New York, NY.
Yongqiang
Y
Tan
Mailman School of Public Health, Columbia University Medical Center, New York, NY.
Wei Yann
WY
Tsai
Department of Radiology, Columbia University Medical Center, New York, NY.
Lawrence H
LH
Schwartz
Department of Radiology, Columbia University Medical Center, New York, NY.
Lin
L
Lu