Fault detection method for magnetic resonance imaging spectrometer based on the acquisition and analysis of its output waveforms

Abstract

In the development of a magnetic resonance imaging spectrometer, the equipment fault detection methods are mainly reliant on visual inspection of reconstructed images or k-space data, combined with observation of the output waveforms via an oscilloscope. However, when using the above methods, it may be quite difficult to determine minor design flaws that would produce image ghost or other problems. This article presents a fault detection method that is based on acquisition and analysis of the output waveforms from the spectrometer. While a sequence is running, the spectrometer outputs, including the digital gate and the gradients, are sampled using a data acquisition card. The acquired data is then processed using a high-performance graphic processing unit to allow the feature points, which are the endpoints of the waveform segments in this design, to be extracted. The processing operation is composed of data filtering, differencing, and clustering. Finally, the extracted feature points are compared with the predefined feature points of the sequence to determine any design errors. This method has been used to solve image ghost problems in our home-built spectrometer.