Magnetic Particle Imaging (MPI) is an imaging technique with promising medical applications based on the behavior of superparamagnetic iron oxide nanoparticles. The nonlinear response of the particles to a highly dynamic applied magnetic field induces a voltage in multiple receiving coils, from which a local image of the nanoparticle concentration can be reconstructed. Due to its high temporal and potentially high spatial resolution, MPI is suitable for a wide variety of in vivo applications and does not involve harmful radiation. MPI is currently in the preclinical phase. However, in order to facilitate modeling, data acquisition and reconstruction, some crucial dynamic aspects have been neglected so far.
In this collaborative project, we address three of these aspects, which lead to a variety of dynamical inverse problems: (i) concentration dynamics, (ii) magnetic field dynamics, and (iii) particle magnetization dynamics. Experimental results show that temporal changes in concentration (i) have to be taken into account in the reconstruction step due to the interplay of dynamic processes (such as the heartbeat) and a necessary repetition of sequential measurements to ensure sufficient signal quality. Therefore, our goal is to develop reconstruction methods that explicitly incorporate the dynamic behavior of the concentration to improve reconstruction results in applications such as flow estimation or instrument tracking. Safety requirements limit the amplitudes of the dynamic part of the applied magnetic field, resulting in a limited field of view (FOV) during a measurement cycle. Increasing the FOV and developing dynamic measurement strategies encoded in the applied magnetic field (ii) are of particular interest for future human-scale applications. In this project, we aim to develop a strategy to reduce calibration costs, adaptive sensing methods to efficiently capture the desired object features, and corresponding dynamic reconstruction methods. We further address the still unsolved problem of correctly modeling the system function in MPI. This is related to the magnetization behavior of the particles (iii) in the rapidly changing applied magnetic field. The behavior is mainly determined by Neél rotation mechanisms of large nanoparticle ensembles. We consider dynamic parameter identification problems in extended models for large particle ensembles to enable model-based reconstruction in MPI.
The solution of these various interrelated dynamical problems are of great importance for the further development of MPI methodology to enable entry into the clinical phase.
