International Journal on Magnetic Particle Imaging (IJMPI)
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Concentration Dependent MPI Tracer Performance
No full textMagnetic Particle Imaging (MPI) and Magnetic Particle Spectroscopy (MPS) usually require a reference sample measurement to provide information about the non-linear dynamic magnetic behavior of a specific magnetic nanoparticle (MNP) type. This reference sample based approach presupposes that this dynamic magnetization behavior of MNP is concentration independent. We investigated Resovist® and its precursor Ferucarbotran at different concentrations to verify this assumption by means of MPS. Remarkably, for Resovist® we found a strong concentration dependence of the MPS signal. Above an iron concentration of about 150 mmol/L the shape of the moment and phase spectra changed with increasing iron concentration. In contrast, for Ferucarbotran we found no concentration dependence of the dynamic magnetic behavior even though at a two?fold higher initial concentration. Our experimental results indicate that the dynamic magnetic behavior of MPI tracers may be altered at higher concentrations and should be studied prior to MPI by MPS experiments
Basic Study of Image Reconstruction Method Using Neural Networks with Additional Learning for Magnetic Particle Imaging
No full textIn magnetic particle imaging (MPI), image blurring and artifacts occur in a reconstructed image because the magnetization signals generated from magnetic nanoparticles (MNPs) at the field free point (FFP) are similar to those around the FFP regions. In order to overcome these problems, we proposed a new reconstruction method using neural networks. In this method, a data set of magnetization signals and MNP location pairs is used for learning in neural networks. If all possible combinations of the data sets are learned, an accurate estimated result is obtained. However, it is difficult to learn all the combinations in a reasonable period of time. In this study, the number of data sets learned in the first stage was minimized, and additional learning using the appropriate data sets, which reduces the error between observed signals and estimated signals, was performed. By learning the minimum number of required data sets, it is expected that image blurring and artifacts will be suppressed even when the MNP’s magnetization is insufficient, e.g., when an applied alternative magnetic field and/or a gradient magnetic field are/is weak. We performed numerical experiments to confirm the effectiveness of our proposed method. From the experimental results, it was confirmed that image blurring and artifacts were suppressed using our proposed method even when the MNP’s magnetization was insufficient. However, it may be difficult to reconstruct an accurate image when appropriate data sets are not selected for learning. Hence, in the future, we will improve the method for selecting the data sets
Flexible and Dynamic Patch Reconstruction for Traveling Wave Magnetic Particle Imaging
No full textDifferent types of scanners have been presented since the first publication of Magnetic Particle Imaging (MPI) in 2005. As a result, there are different types of reconstruction methods available, which can be separated into two basic concepts: reconstruction using a system matrix and reconstruction using a direct deconvolution. Both methods have their merits and drawbacks. For the first approach hardware parameters like sampling rate and frequencies have to be chosen carefully to fit the parameter selection process required for the system matrix. For the other approach the temporal and spatial homogeneity of the magnetic field gradient over the entire FOV has to be high to perform an accurate reconstruction, which results in smaller FOVs.In this paper a novel reconstruction method is presented, which combines the advantages of both reconstruction methods to be more flexible during the entire reconstruction process. Furthermore, it enables the possibility of performing a dynamic patch reconstruction, which allows to select arbitrary areas of the FOV for higher resolution reducing reconstruction time significantly.In addition, this new reconstruction improved the image quality of a Traveling Wave MPI scanner substantially
Simultaneous magnetic particle imaging (MPI) and temperature mapping using multi-color MPI
No full textMagnetic Particle Imaging (MPI) is a novel imaging technique based on the non-linear magnetization response of super-paramagnetic iron oxide nanoparticles (SPIOs). It has previously been shown that beside the spatial particle distribution, information about the particle type, its environment, and the particle temperature can be obtained from the magnetic particle signal. Successful separation of particle types and temperature measurements have been reported in spectroscopic experiments, but a simultaneous mapping of the particle distribution and temperature in a spatially encoded imaging experiment has not been performed. This work presents simultaneous imaging and temperature mapping using a ‘multi-color’ reconstruction approach
Launching the new Journal on Magnetic Particle Imaging
No full textIn this editorial, we introduce the new International Journal on Magnetic Particle Imaging (IJMPI) that will be a future platform for publishing high quality research articles on MPI. This journal has its origins in the International Workshop on Magnetic Particle Imaging (IWMPI), which is a unique annual meeting where the scientific MPI community discusses recent highlights of their research. The scope of the IJMPI ranges from imaging sequences and reconstruction over scanner instrumentation and particle developments to pre-clinical and clinical applications. Journal articles will be published online with open access under a Creative Commons License. We encourage the submission of research papers within the scope of the IJMPI from now on in order to share ideas and experiences with a focussed audience.