  Magnetic Particle Imaging (MPI) is a tomographic imaging technique for
determining the spatial distribution of superparamagnetic nanoparticles.
Current MPI systems are capable of imaging iron masses over a wide dynamic
range of more than four orders of magnitude. In theory, this range could be
further increased using adaptive amplifiers, which prevent signal clipping.
While this applies to a single sample, the dynamic range is severely limited if
several samples with different concentrations or strongly inhomogeneous
particle distributions are considered. One scenario that occurs quite
frequently in pre-clinical applications is that a highly concentrated tracer
bolus in the vascular system "shadows" nearby organs with lower effective
tracer concentrations. The root cause of the problem is the ill-posedness of
the MPI imaging operator, which requires regularization for stable
reconstruction. In this work, we introduce a simple two-step algorithm that
increases the dynamic range by a factor of four. Furthermore, the algorithm
enables spatially adaptive regularization, i.e. highly concentrated signals can
be reconstructed with maximum spatial resolution, while low concentrated
signals are strongly regularized to prevent noise amplification.
