  Objective: Magnetic particle imaging (MPI) is an emerging medical imaging
modality which has gained increasing interest in recent years. Among the
benefits of MPI are its high temporal resolution, and that the technique does
not expose the specimen to any kind of ionizing radiation. It is based on the
non-linear response of magnetic nanoparticles to an applied magnetic field.
From the electric signal measured in receive coils, the particle concentration
has to be reconstructed. Due to the ill-posedness of the reconstruction
problem, various regularization methods have been proposed for reconstruction
ranging from early stopping methods, via classical Tikhonov regularization and
iterative methods to modern machine learning approaches. In this work, we
contribute to the latter class: we propose a plug-and-play approach based on a
generic zero-shot denoiser with an $\ell^1$-prior.
  Approach: We validate the reconstruction parameters of the method on a hybrid
dataset and compare it with the baseline Tikhonov, DIP and the previous PP-MPI,
which is a plug-and-play method with denoiser trained on MPI-friendly data.
  Main results: We offer a quantitative and qualitative evaluation of the
zero-shot plug-and-play approach on the 3D Open MPI dataset. Moreover, we show
the quality of the approach with different levels of preprocessing of the data.
  Significance: The proposed method employs a zero-shot denoiser which has not
been trained for the MPI task and therefore saves the cost for training.
Moreover, it offers a method that can be potentially applied in future MPI
contexts.
