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Proceedings Paper

Dependence of radiomics features on CT image acquisition and reconstruction parameters using a cadaveric liver
Author(s): Joseph J. Foy; Inna H. Gertsenshteyn; Hania Al-Hallaq; Samuel G. Armato III; William F. Sensakovic
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Paper Abstract

Studies investigating variations in radiomic features due to changes in image acquisition and reconstruction parameters have focused on non-human phantoms to limit the exposure to human subjects. This study investigated such variations in the computed tomography (CT) scans of a cadaveric human liver. A reference CT scan of a normal cadaveric liver and 16 modified scans were acquired with one parameter changed from the reference for each modified scan: two iterative reconstruction strength levels, one reconstruction kernel, one slice thickness, one coronal view, one sagittal view, one field of view (FOV), two pitch levels, one slice interval, two CTDIvol levels, three kV settings, and one scan acquired using a scanner from another manufacturer. The liver was segmented in each scan, and 142 radiomic features were calculated on each slice across all scans. Unpaired Student’s t-tests assessed significant differences in feature distributions between reference and modified scans for all features after correcting for multiple comparisons (p<0.0004). Assessments were repeated after normalizing pixel value histograms for each scan. Variations in tube voltage, pitch, and slice interval resulted in the smallest number of features reflecting significant differences. Variations in FOV and scan orientation resulted in the largest number of features reflecting significant differences. Fractal features were relatively robust to differences among parameters, while first-order features were sensitive to these differences by comparison. After normalization, first-order features improved agreement between modified and reference scans, while higher-order features were not greatly affected.

Paper Details

Date Published: 16 March 2020
PDF: 8 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113140U (16 March 2020); doi: 10.1117/12.2551155
Show Author Affiliations
Joseph J. Foy, The Univ. of Chicago (United States)
Inna H. Gertsenshteyn, The Univ. of Chicago (United States)
Hania Al-Hallaq, The Univ. of Chicago (United States)
Samuel G. Armato III, The Univ. of Chicago (United States)
William F. Sensakovic, Mayo Clinic (United States)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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