Dynamic Inverse Problems in Magnetic Particle Imaging (D-MPI)
Working Group: | WG Industrial Mathematics |
Leadership: | Dr. Tobias Kluth ((0421) 218-63817, E-Mail: tkluth@math.uni-bremen.de) |
Processor: | Hannes Albers ((0421) 218-63818, E-Mail: halbers@uni-bremen.de) |
Funding: | DFG - Deutsche Forschungsgemeinschaft |
Project partner: | Prof. Dr. Bernadette Hahn, Universität Würzburg |
Time period: | 01.05.2020 - 30.04.2023 |
Magnetic particle imaging (MPI) is an imaging modality with promising medical applications that relies on
the behavior of superparamagnetic iron oxide nanoparticles. The particles’ nonlinear response to a highly
dynamic applied magnetic field induces a voltage measured in multiple receive coils from which an image
showing the spatially dependent concentration of the nanoparticles can be reconstructed. A high temporal
and a potentially high spatial resolution make MPI suitable for several in vivo applications without any
associated harmful radiation.
MPI is currently in the preclinical phase. However, several crucial dynamic aspects have been left out of
consideration so far to simplify modeling, data acquisition and reconstruction. In this collaborative project,we address three dynamic aspects of MPI resulting in a variety of dynamic inverse problems: (i) concentration
dynamics, (ii) magnetic field dynamics, and (iii) particle magnetization dynamics.
Experimental results indicate that temporal changes of the concentration (i) are highly relevant during the
reconstruction due to an interplay of environmental dynamic processes (like the heart beat) and the necessary
repetition of sequential measurements to obtain sufficient signal quality. Thus, we aim for developing
reconstruction methods which take the dynamic behavior of the concentration explicitly into account in
order to significantly improve the reconstruction results in applications like, for example, flow estimation or
instrument tracking.
Safety regulations limit the amplitudes of the dynamic part of the applied magnetic field which results in
a limited field-of-view (FOV) during one measurement cycle. Increasing the FOV and developing dynamic
measurement strategies encoded in the applied magnetic field (ii) is of major interest for current animal-size
and particularly for future human-size applications. In this project, we intend to develop a strategy to reduce
the calibration costs, adaptive sampling schemes to capture the desired features efficiently and respective
dynamic reconstruction algorithms.
We further address the still unsolved problem of modeling the system function in MPI properly. It is related
to the particles’ magnetization behavior (iii) in the rapidly changing applied magnetic field. The behavior is
mainly determined by Neél rotation mechanisms of large ensembles of nanoparticles. We propose to solve
dynamic parameter identification problems in extended models for large ensembles of particles to enable
model-based reconstruction in MPI.
The solution of the different but interrelated dynamic problems addressed in this project are highly relevant
for further developments of the MPI methodology enabling an entry into the clinical phase.