Literature DB >> 29487877

Influence of CT acquisition and reconstruction parameters on radiomic feature reproducibility.

Abhishek Midya1, Jayasree Chakraborty1, Mithat Gönen2, Richard K G Do3, Amber L Simpson1.   

Abstract

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.

Entities:  

Keywords:  computed tomography; dose reduction; quantitative imaging; reproducibility; texture analysis

Year:  2018        PMID: 29487877      PMCID: PMC5812985          DOI: 10.1117/1.JMI.5.1.011020

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  31 in total

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10.  Influence of Adaptive Statistical Iterative Reconstructions on CT Radiomic Features in Oncologic Patients.

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