Multi-Patch Sequences in Magnetic Particle Imaging

In this project we develop multi-patch imaging sequences and reconstruction algorithms for enlarged measuring fields in magnetic particle imaging (MPI). The regular field-of-view (FOV) in MPI is limited due to physiological constraints such as tissue heating and nerve stimulation. In practice typical FOV are in the range of 2x2x1 cm³. In order to scan larger regions it is possible to shift the FOV to different positions and scan various smaller FOV, which can later be combined to a joint 3D dataset. Especially the reconstruction of multi-patch data is a computationally intensive and memory demanding task. In this project we develop algorithms for efficient reconstruction of multi-patch MPI data.

To reduce calibration time and speed up image reconstruction, we have introduced a number of different methods, including reducing the number of system matricessystem matrix warping, and overscan extrapolation.

Sketch of a multi-patch imaging sequence.

Publications

[164737]
Title: Suppression of Motion Artifacts in Multi-Patch Magnetic Particle Imaging of a Phantom with Periodic Motion.
Written by: M. Boberg, N. Gdaniec, M. Möddel, P. Szwargulski, and T. Knopp
in: <em>SIAM Conference on Imaging Science (IS22)</em>. (2022).
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Note: inproceedings, multi-patch, artifact

Abstract: Magnetic particle imaging (MPI) is a tracer based imaging technique, which determines the spatial distribution of superparamagnetic iron oxide nanoparticles with a high spatial and temporal resolution. Therefore, MPI is able to image dynamic tracer distributions like cardiac or respiratory motion in in-vivo experiments. As a matter of fact, the imaging volume covers only a few cubic centimeters due to physiological constraints. To cover larger objects a multi-patch approach is used where the imaging volume is shifted relative to the object. Since this reduces the temporal resolution, motion artifacts can occur during the measurement and reconstruction of dynamic tracer distributions. For periodic motions such as the aforementioned cardiac motion, this problem can be solved by reordering the raw measurement data. In a first step, the motion frequency is calculated by analyzing the raw data without reconstruction and without an additional navigator signal. Afterwards data snippets of the raw data corresponding to a specific motion state are rearranged into a virtual frame by using multiple repetitions of the motion state. Finally, the virtual frames can be reconstructed by standard reconstruction techniques. In our experiments, we successfully reconstructed a rotating phantom with a repetition time of 0.56 s without any motion artifacts, while a single full multi-patch measurement cycle takes at least 0.69 s.