High-quality images are essential for any imaging modality to make a reliable diagnosis, and although MPI is highly sensitive, artifacts are common. This issue poses significant challenges for applications that operate in environments with extremely low levels of iron, such as cell tracking. As a result, our objective is to reduce the amount of image artifacts in MPI by implementing different methods in the reconstruction process that allow for these applications. Key components for artifact reduction are:

• Background subtraction in order to remove disturbing signals (e.g. joint estimation of particle distribution and background)
• Joint reconstruction of multi-patch data to avoid artifacts at patch boundaries
• Multi-level reconstruction methods to enhance contrast (e.g. increased dynamic range)
• Raw data post-processing methods to reduce motion artifacts (e.g. multi-patch rotation)
• Bias-reduction methods for regularization (e.g. bias reduction for sparsity promoting regularization)