Magnetic Particle Imaging (MPI) is an emerging medical imaging modality that detects superparamagnetic particles exploiting their nonlinear magnetization response . Spatial encoding can be realized using a Field Free Line (FFL), which is generated, rotated and translated through the Field of View (FOV) using a combination of magnetic gradient fields and homogeneous excitation fields . This allows for system function based or Radon-based image reconstruction . When scaling up systems [4–8] and/or enlarging the FOV in comparison to the scanner bore , ensuring homogeneity and linearity of the magnetic fields becomes challenging. Recently, it has been demonstrated in a study comparing two specific scanner setups that field imperfections may lead to artifacts in FFL imaging using Radon-based image reconstruction . The underlying effect is well-known from other imaging modalities: Because of the field inhomogeneities/nonlinearities, assumptions made in Radon-based image reconstruction  are violated, which may lead to image artifacts . The present contribution describes the first comprehensive and systematic study on the influence of magnetic field imperfections in FFL MPI. Preliminary results of the study have been presented in .
In a simulation study using proprietary software , 14 different FFL scanner setups have been examined using parameters similar to . Starting from an ideal scanner using perfect magnetic fields, defined imperfections have been introduced in a range of configurations:
– Nonlinear gradient fields (using Maxwell coils and coils with a radius of 75 %, 50 % and 25 % thereof),
– Inhomogeneous excitation fields (using Helmholtz coils and coils with a radius of 75 %, 50 % and 25 % thereof),
– Inhomogeneous receive fields (using Helmholtz coils and coils with a radius of 75 %, 50 % and 25 % thereof),
– A combination of nonlinear gradient fields (Maxwell50) and inhomogeneous excitation fields (Helmholtz50).
In the first part of the study, the voltage induced in the receive channels parallel and perpendicular to the FFL translation have been studied for discrete FFL angles. In the second part, an imaging process has been simulated comparing different image reconstruction approaches. Image reconstruction was performed using both a system function based approach and a Radon-based approach, the latter with and without deconvolution using the point spread function (PSF).
The induced voltage signals (see Figs. 1 and 2) demonstrate illustratively the effect of the magnetic field imperfections. Compared to the ideal scanner, the voltage peaks occur with deformed peak shapes. Remarkably, a field imperfection may lead to a signal being detectable in the receive channel perpendicular to the FFL movement.
Fig. 3 shows the MPI images reconstructed using different techniques. In images reconstructed using a Radon-based approach, the magnetic field imperfections lead to pronounced artifacts, especially if a deconvolution using the point spread function is performed. The artifacts are less prominent in the center than in the rest of the FOV. In images reconstructed using a system function based approach, variations in local image quality become visible.
Radon-based image reconstruction in FFL MPI in the presence of inhomogeneous and nonlinear magnetic fields is prone to severe artifacts, therefore artifact correction methods will have to be developed. In this regard, a first approach has recently been presented by Murase et al. . Moreover, a method to predict the extent of artifacts to be expected based on magnetic field properties would be desirable as well.
Further research is required to elucidate the influence of magnetic field imperfections in MPI using a system function based image reconstruction approach. This is subject of ongoing studies.
The authors gratefully acknowledge the financial support of the German Federal Ministry of Education and Research (BMBF) under grant numbers 13N11090, 13EZ1140A/B and 01EZ0912, of the European Union and the State Schleswig-Holstein (Programme for the Future – Economy) under grant number 122-10-004, of the German Research Foundation (DFG) under grant number BU 1436/7-1 and of the Graduate School for Computing in Medicine and Life Sciences funded by Germany’s Excellence Initiative [DFG GSC 235/2].
Conflict of interest: Authors state no conflict of interest. Material and Methods: Informed consent: Informed consent has been obtained from all individuals included in this study. Ethical approval: The research related to human use has been complied with all the relevant national regulations, institutional policies and in accordance the tenets of the Helsinki Declaration, and has been approved by the authors’ institutional review board or equivalent committee.
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