An Automated Method for Quality Control in MRI Systems: Methods and Considerations

• Published in: Journal of imaging Vol. 6; no. 10; p. 111
• Main Authors: Epistatou, Angeliki C., Tsalafoutas, Ioannis A., Delibasis, Konstantinos K.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 18-10-2020; MDPI
• Subjects: Acceptance tests; Accreditation; ACR phantom; automated method; Automation; Ghosting; Magnetic resonance imaging; Mathematical analysis; Medical imaging; Methods; MRI quality control; NMR; Nuclear magnetic resonance; Parameter sensitivity; Quality control; Radiation; Radiology; Random variables; randomization; Reliability analysis; Reproducibility; Signal to noise ratio; SNR; SNR uniformity; Software; United States > US
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging6100111

Objective: The purpose of this study was to develop an automated method for performing quality control (QC) tests in magnetic resonance imaging (MRI) systems, investigate the effect of different definitions of QC parameters and its sensitivity with respect to variations in regions of interest (ROI) positioning, and validate the reliability of the automated method by comparison with results from manual evaluations. Materials and Methods: Magnetic Resonance imaging MRI used for acceptance and routine QC tests from five MRI systems were selected. All QC tests were performed using the American College of Radiology (ACR) MRI accreditation phantom. The only selection criterion was that in the same QC test, images from two identical sequential sequences should be available. The study was focused on four QC parameters: percent signal ghosting (PSG), percent image uniformity (PIU), signal-to-noise ratio (SNR), and SNR uniformity (SNRU), whose values are calculated using the mean signal and the standard deviation of ROIs defined within the phantom image or in the background. The variability of manual ROIs placement was emulated by the software using random variables that follow appropriate normal distributions. Results: Twenty-one paired sequences were employed. The automated test results for PIU were in good agreement with manual results. However, the PSG values were found to vary depending on the selection of ROIs with respect to the phantom. The values of SNR and SNRU also vary significantly, depending on the combination of the two out of the four standard rectangular ROIs. Furthermore, the methodology used for SNR and SNRU calculation also had significant effect on the results. Conclusions: The automated method standardizes the position of ROIs with respect to the ACR phantom image and allows for reproducible QC results.