Magnetic particle imaging (MPI) is a tomographic imaging modality sensitive to the spatial distribution of magnetic particles. The spectrometer, described in this paper, is capable of measuring the remagnetization spectrum of superparamagnetic nanoparticles. With this spectrometer the suitability of particles, for MPI, can be characterized. Furthermore, the spectrometer can be used to estimate the particle size distribution, which allows for more accurate simulations in MPI.

Recently a new imaging technique called magnetic particle imaging was proposed. The method uses the nonlinear response of magnetic nanoparticles when a time varying magnetic field is applied. Spatial encoding is achieved by moving a field-free point through an object of interest while the field strength in the vicinity of the point is high. A resolution in the submillimeter range is provided even for fast data acquisition sequences. In this paper, a simulation study is performed on different trajectories moving the field-free point through the field of view. The purpose is to provide mandatory information for the design of a magnetic particle imaging scanner. Trajectories are compared with respect to density, speed and image quality when applied in data acquisition. Since simulation of the involved physics is a time demanding task, moreover, an efficient implementation is presented utilizing caching techniques.

Magnetic particle imaging (MPI) is a new imaging technique capable of imaging the distribution of superparamagnetic particles at high spatial and temporal resolution. For the reconstruction of the particle distribution, a system of linear equations has to be solved. The mathematical solution to this linear system can be obtained using a least-squares approach. In this paper, it is shown that the quality of the least-squares solution can be improved by incorporating a weighting matrix using the reciprocal of the matrix-row energy as weights. A further benefit of this weighting is that iterative algorithms, such as the conjugate gradient method, converge rapidly yielding the same image quality as obtained by singular value decomposition in only a few iterations. Thus, the weighting strategy in combination with the conjugate gradient method improves the image quality and substantially shortens the reconstruction time. The performance of weighting strategy and reconstruction algorithms is assessed with experimental data of a 2D MPI scanner.

Magnetic particle imaging (MPI) is a new imaging modality capable of imaging distributions of superparamagnetic nanoparticles with high sensitivity, high spatial resolution and, in particular, high imaging speed. The image reconstruction process requires a system function, describing the mapping between particle distribution and acquired signal. To date, the system function is acquired in a tedious calibration procedure by sequentially measuring the signal of a delta sample at the positions of a grid that covers the field of view. In this work, for the first time, the system function is calculated using a model of the signal chain. The modeled system function allows for reconstruction of the particle distribution in a 1-D MPI experiment. The approach thus enables fast generation of system functions on arbitrarily dense grids. Furthermore, reduction in memory requirements may be feasible by generating parts of the system function on the fly during reconstruction instead of keeping the complete matrix in memory.

Recently, a new imaging modality called magnetic particle imaging (MPI) was introduced. The method is capable of imaging the distribution of superparamagnetic nanoparticles at high sensitivity, high resolution and high imaging speed by exploiting their non-linear magnetization curve. Up to now, all published simulation as well as experimental work uses a scanner setup, where the field of view (FOV) lies in between a symmetric coil configuration. This, however, poses a size limitation for the specimens. In this paper, we present a feasibility study of a new, so-called single-sided scanner, which is applied to the object of interest merely from one side. Thus, the problem of the specimen fitting into the scanner no longer exists, which denotes a major step for MPI. To date, the FOV of the single-sided device is limited to one dimension. First experimental results on imaging phantoms containing a superparamagnetic fluid show a resolution of up to 1 mm and are indeed promising.

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The magnetic particle imaging method allows for the quantitative determination of spatial distributions of superparamagnetic nanoparticles in vivo. Recently, it was shown that the 1-D magnetic particle imaging process can be formulated as a convolution. Analyzing the width of the convolution kernel allows for predicting the spatial resolution of the method. However, this measure does not take into account the noise of the measured data. Furthermore, it does not consider a reconstruction step, which can increase the resolution beyond the width of the convolution kernel. In this paper, the spatial resolution of magnetic particle imaging is investigated by analyzing the modulation transfer function of the imaging process. An expression for the spatial resolution is derived, which includes the noise level and which is validated in simulations and experiments.

The model-based system function approach addresses a major drawback of the measurement-based procedure, namely, the long acquisition time. In this work, the acquisition of the measurement-based system function took 45 min, while the model-based system function was obtained in only 15 s. For 3D data, where the acquisition of the measurement-based system function takes more than 6 h, the need for an efficient system function generation is even more obvious.