14 December 2017 Accounting for reconstruction kernel-induced variability in CT radiomic features using noise power spectra
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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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Muhammad Shafiq-ul-Hassan, Geoffrey G. Zhang, Dylan C. Hunt, Kujtim Latifi, Ghanim Ullah, Robert J. Gillies, Eduardo G. Moros, "Accounting for reconstruction kernel-induced variability in CT radiomic features using noise power spectra," Journal of Medical Imaging 5(1), 011013 (14 December 2017). https://doi.org/10.1117/1.JMI.5.1.011013 . Submission: Received: 30 June 2017; Accepted: 21 November 2017
Received: 30 June 2017; Accepted: 21 November 2017; Published: 14 December 2017

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