  Magnetic particle imaging (MPI) is an imaging modality exploiting the
nonlinear magnetization behavior of (super-)paramagnetic nanoparticles to
obtain a space- and often also time-dependent concentration of a tracer
consisting of these nanoparticles. MPI has a continuously increasing number of
potential medical applications. One prerequisite for successful performance in
these applications is a proper solution to the image reconstruction problem.
More classical methods from inverse problems theory, as well as novel
approaches from the field of machine learning, have the potential to deliver
high-quality reconstructions in MPI. We investigate a novel reconstruction
approach based on a deep image prior, which builds on representing the solution
by a deep neural network. Novel approaches, as well as variational and
iterative regularization techniques, are compared quantitatively in terms of
peak signal-to-noise ratios and structural similarity indices on the publicly
available Open MPI dataset.
