Deep learning based MPI system matrix recovery to increase the spatial resolution of reconstructed images

Magnetic particle imaging (MPI) data is commonly reconstructed using a system matrix acquired in a time-consuming calibration measurement. The calibration approach has the important advantage over model-based reconstruction that it takes the complex particle physics as well as system imperfections into account. This benefit comes for the cost that the system matrix needs to be re-calibrated whenever the scan parameters, particle types or even the particle environment (e.g. viscosity or temperature) changes. One route for reducing the calibration time is the sampling of the system matrix at a subset of the spatial positions of the intended field-of-view and employing system matrix recovery. Recent approaches used compressed sensing (CS) and achieved subsampling factors up to 28 that still allowed reconstructing MPI images of sufficient quality. In this work, we propose a novel framework with ComplexRGB-Loss and a 3d-System Matrix Recovery Network. We demonstrate that the 3d-SMRnet can recover a 3d system matrix with a subsampling factor of 64 in less than one minute. Furthermore, 3d-SMRnet outperforms CS in terms of system matrix quality, reconstructed image quality, and processing time. The advantage of our method is demonstrated by reconstructing open access MPI datasets. The model is further shown to be capable of inferring system matrices for different particle types.