Dynamic image reconstruction in MPI with RESESOP-Kaczmarz

Abstract

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.