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Magnetic Particle Imaging is a scanning modality that exploits the response of superparamagnetic nanoparticles to a dynamic magnetic field. In the multivariate case measurement-based reconstruction approaches are common and involve a system-matrix whose acquisition is time consuming and needs to be repeated whenever the scanning setup changes. Our approach uses a mathematical model, based on the MPI encoding of the signal, that is in particular independent of the scanning trajectories. We present basic ways of leveraging this independence property to enhance the quality of the reconstruction by using data from different scans. We demonstrate the potential of our approach with examples. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Magnetic Particle Imaging (MPI) is an emerging imaging modality and is currently 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 we introduced in an earlier work and which is based on the MPI signal encoding. Here, we present an enhanced two-stage reconstruction algorithm derived from the decomposition of the imaging process provided by the model. In the first stage of the algorithm we propose a variational formulation together with adequate regularization which improves our reconstruction results. [ABSTRACT FROM AUTHOR]