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dc.creatorEpistatou A.C., Tsalafoutas I.A., Delibasis K.K.en
dc.date.accessioned2023-01-31T07:37:28Z
dc.date.available2023-01-31T07:37:28Z
dc.date.issued2020
dc.identifier10.3390/jimaging6100111
dc.identifier.issn2313433X
dc.identifier.urihttp://hdl.handle.net/11615/71405
dc.description.abstractObjective: 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. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.en
dc.language.isoenen
dc.sourceJournal of Imagingen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85106889306&doi=10.3390%2fjimaging6100111&partnerID=40&md5=0334551a855c5021b655b85894ca0d33
dc.subjectAcceptance testsen
dc.subjectAutomationen
dc.subjectMagnetic resonance imagingen
dc.subjectNormal distributionen
dc.subjectPhantomsen
dc.subjectQuality assuranceen
dc.subjectQuality controlen
dc.subjectSensitivity analysisen
dc.subjectAmerican college of radiologiesen
dc.subjectAmerican college of radiology phantomen
dc.subjectAutomated methodsen
dc.subjectImage uniformityen
dc.subjectImaging qualityen
dc.subjectMagnetic resonance imaging quality controlen
dc.subjectPercentage ghosting ratioen
dc.subjectPercentage image uniformityen
dc.subjectPhantomsen
dc.subjectRandomisationen
dc.subjectSignal-to-noise ratio uniformityen
dc.subjectSignal to noise ratioen
dc.subjectMDPIen
dc.titleAn Automated Method for Quality Control in MRI Systems: Methods and Considerationsen
dc.typejournalArticleen


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