Open AccessTechnical Note: Proof of concept for radiomics‐based quality assurance for computed tomography
Author(s) -
Branco Luciano R. F.,
Ger Rachel B.,
Mackin Dennis S.,
Zhou Shouhao,
Court Laurence E.,
Layman Rick R.
Publication year - 2019
Publication title -
journal of applied clinical medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.83
H-Index - 48
ISSN - 1526-9914
DOI - 10.1002/acm2.12750
Subject(s) - scanner , imaging phantom , quality assurance , artifact (error) , feature (linguistics) , artificial intelligence , computer science , nuclear medicine , cartridge , image quality , population , computer vision , pattern recognition (psychology) , medicine , engineering , mechanical engineering , linguistics , philosophy , external quality assessment , environmental health , pathology , image (mathematics)
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.