Accounting for Reconstruction Kernel-induced Variability in CT Radiomic Features using Noise Power Spectra

Authors

Muhammad Shafiq ul Hassan, University of South FloridaFollow
Geoffrey Zhang, University of South FloridaFollow
Dylan C. Hunt, H. Lee Moffitt Cancer Center
Kujtim Latifi, University of South FloridaFollow
Ghanim Ullah, University of South FloridaFollow
Robert J. Gillies, H. Lee Moffitt Cancer Center and Research InstituteFollow
Eduardo G. Moros, University of South FloridaFollow

Document Type

Article

Publication Date

12-2017

Keywords

Computed tomography, Scanners, Spatial frequencies, CT reconstruction, Solar thermal energy, Feature extraction, Lung, Cancer, Image quality, Medical imaging

Digital Object Identifier (DOI)

https://doi.org/10.1117/1.JMI.5.1.011013

Abstract

Large variability in computed tomography (CT) radiomics feature values due to CT imaging parameters can have subsequent implications on the prognostic or predictive significance of these features. Here, we investigated the impact of pitch, dose, and reconstruction kernel on CT radiomic features. Moreover, we introduced correction factors to reduce feature variability introduced by reconstruction kernels. The credence cartridge radiomics and American College of Radiology (ACR) phantoms were scanned on five different scanners. ACR phantom was used for 3-D noise power spectrum (NPS) measurements to quantify correlated noise. The coefficient of variation (COV) was used as the variability assessment metric. The variability in texture features due to different kernels was reduced by applying the NPS peak frequency and region of interest (ROI) maximum intensity as correction factors. Most texture features were dose independent but were strongly kernel dependent, which is demonstrated by a significant shift in NPS peak frequency among kernels. Percentage improvement in robustness was calculated for each feature from original and corrected %COV values. Percentage improvements in robustness of 19 features were in the range of 30% to 78% after corrections. We show that NPS peak frequency and ROI maximum intensity can be used as correction factors to reduce variability in CT texture feature values due to reconstruction kernels.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

Journal of Medical Imaging, v. 5, issue 1, art. 011013

Scholar Commons Citation

Shafiq ul Hassan, Muhammad; Zhang, Geoffrey; Hunt, Dylan C.; Latifi, Kujtim; Ullah, Ghanim; Gillies, Robert J.; and Moros, Eduardo G., "Accounting for Reconstruction Kernel-induced Variability in CT Radiomic Features using Noise Power Spectra" (2017). Physics Faculty Publications. 71.
https://digitalcommons.usf.edu/phy_facpub/71