Image-Based Detection of MRI Hardware Failures

Currently in Magnetic Resonance Imaging (MRI) systems, most hardware failures are only detected after a component has stopped functioning properly. In many cases, this results in a downtime of the system. Moreover, sometimes defective parts are not identified correctly, which may result in more parts than necessary being replaced, causing extra costs. Often in MRI systems, hardware related problems have an impact on image quality. Given an imaging protocol and a well-functioning MRI system, certain image quality metrics have a normal range in a given patient population. Thus, such metrics will present a measurable behavior change in case of a hardware problem. We identified such simple and powerful metrics for signal-to-noise ratio, noise variance and symmetry in images for hardware failures related to Shimming and Local RF coils in this work. To be able to calculate these metrics with every MRI image during the clinical workflow, another constraint is the computation time. With the performance of quality metrics on machine learning algorithms and computation time, we are able to identify the failing MRI components with an accuracy of up to 0.96 AUROC.