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The image reconstruction in Magnetic Particle Imaging (MPI) relies on efficiently solving an ill-posed inverse problem. Current state-of-the-art reconstruction methods are either based on row-action methods with fast convergence but limited noise suppression or advanced sparsity constraints showing better image quality, but suffering from a higher computational complexity and slower convergence. In this contribution, we propose a novel row-action framework where advanced sparsity constraints, e.g., a combination of L1- and TV-norm, can be included. Its performance is numerically evaluated on simulated and real MPI data, showing a significant reduction of computation time while retaining the enhanced imaging quality.
Int. J. Mag. Part. Imag. 6(2), Suppl. 1, 2020, Article ID: 2009002, DOI: 10.18416/IJMPI.2020.2009002