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Magnetic particle imaging (MPI) is an imaging modality exploiting thenonlinear magnetization behavior of (super-)paramagnetic nanoparticles toobtain a space- and often also time-dependent concentration of a tracerconsisting of these nanoparticles. MPI has a continuously increasing number ofpotential medical applications. One prerequisite for successful performance inthese applications is a proper solution to the image reconstruction problem.More classical methods from inverse problems theory, as well as novelapproaches from the field of machine learning, have the potential to deliverhigh-quality reconstructions in MPI. We investigate a novel reconstructionapproach based on a deep image prior, which builds on representing the solutionby a deep neural network. Novel approaches, as well as variational anditerative regularization techniques, are compared quantitatively in terms ofpeak signal-to-noise ratios and structural similarity indices on the publiclyavailable Open MPI dataset.

Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset