Motion-corrected image reconstruction from dynamic MPI data

Abstract

Magnetic Particle Imaging (MPI) is an emerging imaging modality that offers unique insights into the structures under examination. Typically, it is assumed that the studied specimen is stationary. In practical applications however, the 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 reconstruction of the data might result in motion artifacts and compromised image quality.

This talk will focus on the reconstruction from simulated as well as real dynamic MPI data. We introduce and assess different methods designed to approximate high-quality images in the presence of motion. A promising technique provides the regularized sequential subspace optimization (RESESOP) algorithm, which does only need few a priori information and little additional computational resources for improved reconstructions. All results will be compared against established algorithms like regularized Kaczmarz.