A flexible model-based regularized reconstruction approach for magnetic particle imaging

Magnetic Particle Imaging (MPI) is an emerging imaging modality and a very active field of research. In the multivariate MPI setup images are usually reconstructed using a system matrix which is obtained by a time-consuming measurement procedure. We approach the reconstruction problem by employing a mathematical model which is based on the MPI signal encoding and the properties of the MPI Core Operator. Here, we present a reconstruction algorithm which features two stages: in the first stage, we estimate components of the MPI Core Operator by using a variational formulation. In the second stage, the image is reconstructed by regularized deconvolution applied to the components of the MPI Core Operator robustly estimated in the first stage. We demonstrate the potential of our algorithm with examples.