Biomedical Research

Quality control of Magnetic Resonance Imaging (MRI) equipment based on the American College of Radiology (ACR) protocol is a crucial tool for assuring the optimal functionality of the MRI scanner. This protocol includes the test of several parameters in particular spatial properties such as Slice position accuracy, high-contrast spatial resolution, and slice thickness accuracy. Those parameters are usually evaluated visually and manually using a workstation. As we know that Visual assessment is operator dependant and results may vary, so making the process automatic will reduce the errors and improve the accuracy. We propose in this study an automatic algorithm which is able to extract the three spatial proprieties. We tested our automated quality control process on ten MRI scanners. We assessed the performance of the proposed pipeline by comparing the measurements with the manual analysis made by three experienced MRI technologists. We also compared the results against the recommended values provided by the ACR quality assurance manual. Afterward, all data sets are quantitatively evaluated using the Pearson test by computing the correlation coefficient and the p-value. The comparison between our automatic quantitative algorithm and manual evaluation confirms the effectiveness of the proposed approaches. Accordingly, the results of the Pearson test showed that automated analysis correlated highly with manual analysis and the P-value was less than the significance level of 0.05. An interesting finding was that the total analysis time was significantly shorter than the time needed for manual analysis.

Author(s): Ines Ben Alaya, Mondher Telmoudi, Rania Guesmi, Mokhtar Mars
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