An evaluation of the current status of quality assurance program in MRI: A multicenter study

Introduction: Quality Assurance Programs (QAPs) in Magnetic Resonance Imaging (MRI) are essential for acquiring images that provide information for medical diagnosis and treatment. As part of a quality assurance program, quality control tests (QC) are conducted to ensure that high-quality images are acquired during the acquisition process. Implementing QAPs in MRI is crucial to ensuring diagnostic image acquisition and personnel safety. Methods: In this study, 21 indicators of compliance/non-compliance were used to evaluate the QAP in ten Mexican MRI facilities, through a documentary review. Also, nine QC tests were developed under ACR protocol in the MRIs scanners including 1.5 T and 3.0 T systems. The association of the QAP and QC tests results for MRI scanners was studied. Results: It was found that all facilities evaluated failed at least one of the QAP indicators. Only 20% of the facilities achieved more than 90 % on all QAP indicators, while 30 % achieved 100 % acceptability in QC tests. The statistical analysis revealed a significant positive correlation between the QAP indicators and QC tests, particularly the room technical factors and MRI scanners. Conclusion: The results indicate that each MRI facility needs to improve QAP implementation and surveillance to establish QC reference values to enhance operational conditions and ensure consistent image quality throughout the facilities. It is imperative that the Mexican government update the legal framework regarding MRI safety in accordance with international recommendations and guidelines in order to ensure the implementation of QAPs in MRI facilities. Implications for practice: As a result of standardizing protocols and monitoring reference values for image quality, the implementation of QA and QC indicators in MRI facilities may improve clinical practice and patient safety. (c) 2025 The College of Radiographers. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.