  In Magnetic Particle Imaging (MPI), it is typically assumed that the studied
specimen is stationary during the data acquisition. In practical applications
however, the searched-for 3D distribution of the magnetic nanoparticles might
show a dynamic behavior, caused by e.g. breathing or movement of the blood.
Neglecting those dynamics during the reconstruction step results in motion
artifacts and a reduced image quality.
  This article addresses the challenge of capturing high quality images in the
presence of motion. A promising technique provides the Regularized Sequential
Subspace Optimization (RESESOP) algorithm, which takes dynamics as model
inexactness into account, significantly improving reconstruction compared to
standard static algorithms like regularized Kaczmarz. Notably, this algorithm
operates with minimal prior information and the method allows for subframe
reconstruction, making it suitable for scenarios with rapid particle movement.
The performance of the proposed method is demonstrated on both simulated and
real data sets.
