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This paper presents an approach for acquiring a magnetic particle imaging (MPI) signal, by utilizing the second harmonic detection of the magnetic nanoparticles tracers. An MPI signal with high signal-to-noise ratio (SNR) is crucial for high spatial resolution images that reveals the distribution of the tracers in a target area. Samples of Resovist and Perimag nanoparticles tracers were prepared in liquid and immobilised form, which were placed at some distances under the receiver coil of the single-sided MPI scanner. The samples were exposed to the excitation magnetic field generated at 22.8 kHz and a static gradient field generated with a direct current of 2 A. The non-linear magnetization response of the tracers for each spatial position is recorded in the form of voltage signal by a gradiometer pickup coil, with the second harmonic signal being extracted by a resonance circuit. The results obtained revealed that a sufficient signal from the tracers is recorded at up to 25 mm under the pickup coil, with Perimag samples inducing higher signals as compared to Resovist. The dependence of the DC gradient field on the second harmonic signal shows that the peak signal amplitude for Resovist and Perimag particles as ±5 mT and ±6 mT respectively. Additionally, the second harmonic signal amplitude increases exponentially with an increase in the excitation magnetic field. Thus, the results obtained shows the potential of this approach in acquiring high SNR MPI signals at low excitation frequency, which could be vital in reconstructing the contour images of the tracers, particularly in sentinel lymph node biopsy (SLNB) for breast cancer diagnosis. 

We contribute to the mathematical modeling and analysis of magnetic particle imaging which is a promising new in-vivo imaging modality. Concerning modeling, we develop a structured decomposition of the imaging process and extract its core part which we reveal to be common to all previous contributions in this context. The central contribution of this paper is the development of reconstruction formulae for MPI in 2D and 3D. Until now, in the multivariate setup, only time consuming measurement approaches are available, whereas reconstruction formulae are only available in 1D. The 2D and the 3D (describing the real world) reconstruction formulae which we derive here are significantly different from the 1D situation -- in particular there is no Dirac property in dimensions greater than one when the particle sizes approach zero. As a further result of our analysis, we conclude that the reconstruction problem in MPI is severely ill-posed. Finally, we obtain a model-based reconstruction algorithm.

Magnetic particle imaging (MPI) is a tomographic method to determine the spatial distribution of magnetic nanoparticles. In this document a file format for the standardized storage of MPI data is introduced. The aim of the Magnetic Particle Imaging Data Format (MDF) is to provide a coherent way of exchanging MPI data acquired with different MPI scanners worldwide. The focus of the file format is on sequence parameters, raw measurement data, calibration data, and reconstruction data. The format is based on the hierarchical document format (HDF) in version 5 (HDF5).

This article is from International Journal of Nanomedicine , volume 9 . Abstract Background: Magnetic particle imaging (MPI) uses magnetic fields to visualize superparamagnetic iron oxide nanoparticles (SPIO). Today, Resovist® is still the reference SPIO for MPI. The objective of this study was to evaluate the in vivo blood half-life of two different types of Resovist (one from Bayer Pharma AG, and one from I’rom Pharmaceutical Co Ltd) in MPI. Methods: A Resovist concentration of 50 μmol/kg was injected into the ear artery of ten New Zealand White rabbits. Five animals received Resovist distributed by I’rom Pharmaceutical Co Ltd and five received Resovist by Bayer Pharma AG. Blood samples were drawn before and directly after injection of Resovist, at 5, 10, and 15 minutes, and then every 15 minutes until 120 minutes after the injection. The MPI signal of the blood samples was evaluated using magnetic particle spectroscopy. Results: The average decline of the blood MPI signal from the two distributions differed significantly (P=0.0056). Resovist distributed by Bayer Pharma AG showed a slower decline of the MPI signal (39.7% after 5 minutes, 20.5% after 10 minutes, and 12.1% after 15 minutes) compared with Resovist produced by I’rom Pharmaceutical Co Ltd (20.4% after 5 minutes, 7.8% after 10 minutes, no signal above noise level after 15 minutes). Conclusion: In MPI, the blood half-life of an SPIO tracer cannot be equalized to the blood half-life of its MPI signal. Resovist shows a very rapid decline of blood MPI signal and is thus not suitable as a long circulating tracer. For cardiovascular applications in MPI, it may be used as a bolus tracer.

This article is from International Journal of Molecular Sciences , volume 14 . Abstract Magnetic particle imaging (MPI) is a promising medical imaging technique producing quantitative images of the distribution of tracer materials (superparamagnetic nanoparticles) without interference from the anatomical background of the imaging objects (either phantoms or lab animals). Theoretically, the MPI platform can image with relatively high temporal and spatial resolution and sensitivity. In practice, the quality of the MPI images hinges on both the applied magnetic field and the properties of the tracer nanoparticles. Langevin theory can model the performance of superparamagnetic nanoparticles and predict the crucial influence of nanoparticle core size on the MPI signal. In addition, the core size distribution, anisotropy of the magnetic core and surface modification of the superparamagnetic nanoparticles also determine the spatial resolution and sensitivity of the MPI images. As a result, through rational design of superparamagnetic nanoparticles, the performance of MPI could be effectively optimized. In this review, the performance of superparamagnetic nanoparticles in MPI is investigated. Rational synthesis and modification of superparamagnetic nanoparticles are discussed and summarized. The potential medical application areas for MPI, including cardiovascular system, oncology, stem cell tracking and immune related imaging are also analyzed and forecasted.