Ms. Inna H. Gertsenshteyn Profile

Inna H. Gertsenshteyn

PhD Student

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Proceedings Article | 16 March 2020 Presentation + Paper

Dependence of radiomics features on CT image acquisition and reconstruction parameters using a cadaveric liver

Joseph Foy, Inna Gertsenshteyn, Hania Al-Hallaq, Samuel Armato, William Sensakovic

Proceedings Volume 11314, 113140U (2020) https://doi.org/10.1117/12.2551155

KEYWORDS: Image acquisition, Liver, Computed tomography, Feature extraction, CT reconstruction, Fractal analysis, Magnetic resonance imaging, Tissues, Manufacturing, Radiology

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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.

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