Compensation of periodic motion for averaging of magnetic particle imaging data

The temporal resolution of magnetic particle imaging (MPI) is sufficiently high to capture dynamic processes like cardiac motion. The achievable spatial resolution of MPI is closely linked to the signal-to-noise ratio of the measured voltage signal. Therefore, in practice it can be advantageous to improve the signal-to-noise ratio by block-wise averaging the signal over time. However, this will decrease the temporal resolution such that cardiac motion is not resolved anymore. In the present work, we introduce a framework for averaging MPI data that exhibit periodic motion induced by e.g. respiration and/or the heart beat. The frequency of motion is directly derived from the MPI raw data without the need for an additional navigator signal. The short time Fourier transform is used for this purpose, because each of these periodic movements will have a frequency varying over time. In order to average the captured frames corresponding to the same phase of the motion, one has to calculate virtual frames since the data acquisition and the periodic motion are not synchronized. In a phantom study it is shown that the developed method is capable of averaging experimental data without introducing any motion artifacts.