Technical Note: Proof of concept for radiomics‐based quality assurance for computed tomography

Abstract

Purpose: Routine quality assurance (QA) testing to identify malfunctions in medical imaging devices is a standard practice and plays an important role in meeting quality standards. However, current daily computed tomography (CT) QA techniques have proven to be inadequate for the detection of subtle artifacts on scans. Therefore, we investigated the ability of a radiomics phantom to detect subtle artifacts not detected in conventional daily QA.Methods: An updated credence cartridge radiomics phantom was used in this study, with a focus on two of the cartridges (rubber and cork) in the phantom. The phantom was scanned using a Siemens Definition Flash CT scanner, which was reported to produce a subtle line pattern artifact. Images were then imported into the IBEX software program, and 49 features were extracted from the two cartridges using four different preprocessing techniques. Each feature was then compared with features for the same scanner several months previously and with features from controlled CT scans obtained using 100 scanners.Results: Of 196 total features for the test scanner, 79 (40%) from the rubber cartridge and 70 (36%) from the cork cartridge were three or more standard deviations away from the mean of the controlled scan population data. Feature values for the artifact-producing scanner were closer to the population mean when features were preprocessed with Butterworth smoothing. The feature most sensitive to the artifact was co-occurrence matrix maximum probability. The deviation from the mean for this feature was more than seven times greater when the scanner was malfunctioning (7.56 versus 1.01).Conclusions: Radiomics features extracted from a texture phantom were able to identify an artifact-producing scanner as an outlier among 100 CT scanners. This preliminary analysis demonstrated the potential of radiomics in CT QA to identify subtle artifacts not detected using the currently employed daily QA techniques.

Reliability of CT radiomic features reflecting tumour heterogeneity according to image quality and image processing parameters

“…Although a number of studies have addressed issues regarding the reliability of RFs under varying parameters of image acquisition, processing, segmentation, and feature extraction 13,[15][16][17]24,25,27,33 , there have been few studies discussing the interactions between quantization range, bin number, SNR, and outlier inclusion, as performed in our study. We evaluated histograms and probability matrices under varying parameters, demonstrating their potential interactions, to improve the understanding of the fundamental mechanisms of variability.…”

Radiomics feature robustness as measured using an MRI phantom

“…In this study, we performed image preprocessing before extracting radiomics features to reduce uncertainty in the feature analysis; an edge-preserving smoothing filter was applied to the tumor volume before the feature calculations to preserve meaningful edge information while smoothing out undesirable imaging noise 29 . Then, we extracted a total 76 radiomics features from delineated ROIs from MR images of the phantom, volunteer, and patients, respectively.…”

The impact of phantom design and material‐dependence on repeatability and reproducibility of CT‐based radiomics features

“…This is likely the reason why conflicting results were reported in prior studies. 24,35 For example, the previous CCR phantom was customized for non-small cell lung carcinoma CT data. 10 It is understandable the results showed tube current has minimal influence on radiomics feature reproducibility.…”