15 February 2018 Influence of CT acquisition and reconstruction parameters on radiomic feature reproducibility
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High-dimensional imaging features extracted from diagnostic imaging, called radiomics, are increasingly reported for diagnosis, prognosis, and response to therapy. Establishing the sensitivity of radiomic features to variation in scan protocols is necessary because acquisition and reconstruction parameters can vary widely across and within institutions. Our objective was to assess the reproducibility of radiomic features derived from computed tomography (CT) images by varying tube current (mA), noise index, and reconstruction [adaptive statistical iterative reconstruction (ASiR)], parameters increasingly varied by institutions seeking to reduce radiation dose in their patients. We extracted radiomic features from CT images of a uniform water phantom, anthropomorphic phantom, and a human scan. Scans were acquired from the phantoms with six tube currents (50, 100, 200, 300, 400, and 500 mA) and five noise index levels (12, 14, 16, 18, and 20), respectively. Scans of the phantoms and patient were reconstructed from 0% ASiR (i.e., filtered back projection) to 100% ASiR in increments of 10%. Two hundred and forty-eight well-known radiomic features were extracted from all scans. The concordance correlation coefficient was used to assess agreement of features. Our analysis suggests that image acquisition parameters (tube current, noise index) as well as the reconstruction technique strongly influence radiomic feature reproducibility and demonstrate a subset of reproducible features potentially usable in clinical practice.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Abhishek Midya, Jayasree Chakraborty, Mithat Gönen, Richard K. G. Do, Amber L. Simpson, "Influence of CT acquisition and reconstruction parameters on radiomic feature reproducibility," Journal of Medical Imaging 5(1), 011020 (15 February 2018). https://doi.org/10.1117/1.JMI.5.1.011020 . Submission: Received: 1 June 2017; Accepted: 23 January 2018
Received: 1 June 2017; Accepted: 23 January 2018; Published: 15 February 2018

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