Determining the brain perfusion is an important task for diagnosis of vascular diseases such as occlusions and intracerebral haemorrhage. Even after successful diagnosis, there is a high risk of restenosis or rebleeding such that patients need intense attention in the days after treatment. Within this work, we present a diagnostic tomographic imager that allows access to brain perfusion quantitatively in short intervals. The device is based on the magnetic particle imaging technology and is designed for human scale. It is highly sensitive and allows the detection of an iron concentration of 263 pmol Fe ml −1 , which is one of the lowest iron concentrations imaged by MPI so far. The imager is self-shielded and can be used in unshielded environments such as intensive care units. In combination with the low technical requirements this opens up a variety of medical applications and would allow monitoring of stroke on intensive care units.

Tomographic imaging has become a mandatory tool for the diagnosis of a majority of diseases in clinical routine. Since each method has its pros and cons, a variety of them is regularly used in clinics to satisfy all application needs. Magnetic particle imaging (MPI) is a relatively new tomographic imaging technique that images magnetic nanoparticles with a high spatiotemporal resolution in a quantitative way, and in turn is highly suited for vascular and targeted imaging. MPI was introduced in 2005 and now enters the preclinical research phase, where medical researchers get access to this new technology and exploit its potential under physiological conditions. Within this paper, we review the development of MPI since its introduction in 2005. Besides an in-depth description of the basic principles, we provide detailed discussions on imaging sequences, reconstruction algorithms, scanner instrumentation and potential medical applications.

Magnetic particle imaging (MPI) is a relatively new tomographic imaging technique using static and oscillating magnetic fields to image the spatial distribution of magnetic nanoparticles. The latter being the contrast MPI has been initially designed for. However, recently it has been shown that MPI can be extended to a multi-contrast method that allows one to simultaneously image the signals of different MPI tracer materials. Additionally, it has been shown that changes in the particles environment, e.g. the viscosity have an impact on the MPI signal and can potentially be used for functional imaging. The purpose of the present work is twofold. First, we generalize the MPI imaging equation to describe different multi-contrast settings in a unified framework. This allows for a more precise interpretation and discussion of results obtained by single-and multi-contrast reconstruction. Second, we propose and validate a method that allows one to determine the viscosity of a small sample from a dual-contrast reconstruction. To this end, we exploit a calibration curve mapping the sample viscosity onto the relative signal weights within the channels of the dual-contrast reconstruction. The latter allows us to experimentally determine the viscosity of the particle environment in the range of 1-51.8 mPa s with a relative methodological error of less than 6%.

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temperature-resolved magnetic particle imaging (Mpi) represents a promising tool for medical imaging applications. in this study an approach based on a single calibration measurement was applied for highlighting the potential of Mpi for monitoring of temperatures during thermal ablation of liver tumors. For this purpose, liver tissue and liver tumor phantoms embedding different superparamagnetic iron oxide nanoparticles (SPION) were prepared, locally heated up to 70 °C and recorded with Mpi. optimal temperature Mpi Spions and a corresponding linear model for temperature calculation were determined. the temporal and spatial temperature distributions were compared with infrared (IR) camera results yielding quantitative agreements with a mean absolute deviation of 1 °C despite mismatches in boundary areas.

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Magnetic particle imaging (MPI) is an imaging modality that detects the response of a distribution of magnetic nanoparticle tracers to alternating magnetic fields. There has recently been exploration into multi-contrast MPI, in which the signal from different tracer materials or environments is separately reconstructed, resulting in multi-channel images that could enable temperature or viscosity quantification. In this work, we apply a multi-contrast reconstruction technique to discriminate between nanoparticle tracers of different core sizes. Three nanoparticle types with core diameters of 21.9 nm, 25.3 nm and 27.7 nm were each imaged at 21 different locations within the scanner field of view. Multi-channel images were reconstructed for each sample and location, with each channel corresponding to one of the three core sizes. For each image, signal weight vectors were calculated, which were then used to classify each image by core size. With a block averaging length of 10 000, the median signal-to-noise ratio was 40 or higher for all three sample types, and a correct prediction rate of 96.7% was achieved, indicating that core size can effectively be predicted using signal weight vector classification with close to 100% accuracy while retaining high MPI image quality. The discrimination of the core size was reliable even when multiple samples of different core sizes were placed in the measuring field.

The temporal resolution of the tomographic imaging method magnetic particle imaging (MPI) is remarkably high. The spatial resolution is degraded for measured voltage signal with low signal-to-noise ratio, because the regularization in the image reconstruction step needs to be increased for system-matrix approaches and for deconvolution steps in x -space approaches. To improve the signal-to-noise ratio, blockwise averaging of the signal over time can be advantageous. However, since block-wise averaging decreases the temporal resolution, it prevents resolving the motion. In this paper, a framework for averaging motion-corrupted MPI raw data is proposed. The motion is considered to be periodic as it is the case for respiration and/or the heartbeat. The same state of motion is thus reached repeatedly in a time series exceeding the repetition time of the motion and can be used for averaging. As the motion process and the acquisition process are, in general, not synchronized, averaging of the captured MPI raw data corresponding to the same state of motion requires to shift the starting point of the individual frames. For high-frequency motion, a higher frame rate is potentially required. To address this issue, a binning method for using only parts of complete frames from a motion cycle is proposed that further reduces the motion artifacts in the final images. The frequency of motion is derived directly from the MPI raw data signal without the need to capture an additional navigator signal. Using a motion phantom, it is shown that the proposed method is capable of averaging experimental data with reduced motion artifacts. The methods are further validated on in-vivo data from mouse experiments to compensate the heartbeat.