Magnetic particle imaging (MPI) is a new tomographic imaging method potentially capable of rapid 3D dynamic imaging of magnetic tracer materials. Until now, only dynamic 2D phantom experiments with high tracer concentrations have been demonstrated. In this letter, first in vivo 3D real-time MPI scans are presented revealing details of a beating mouse heart using a clinically approved concentration of a commercially available MRI contrast agent. A temporal resolution of 21.5 ms is achieved at a 3D field of view of 20.4 x 12 x 16.8 mm(3) with a spatial resolution sufficient to resolve all heart chambers. With these abilities, MPI has taken a huge step toward medical application.

BackgroundMagnetic particle imaging (MPI) is a new tomographic imaging technique capable of imaging magnetic tracer material at high temporal and spatial resolution. Image reconstruction requires solving a system of linear equations, which is characterized by a "system function" that establishes the relation between spatial tracer position and frequency response. This paper for the first time reports on the structure and properties of the MPI system function.MethodsAn analytical derivation of the 1D MPI system function exhibits its explicit dependence on encoding field parameters and tracer properties. Simulations are used to derive properties of the 2D and 3D system function.ResultsIt is found that for ideal tracer particles in a harmonic excitation field and constant selection field gradient, the 1D system function can be represented by Chebyshev polynomials of the second kind. Exact 1D image reconstruction can thus be performed using the Chebyshev transform. More realistic particle magnetization curves can be treated as a convolution of the derivative of the magnetization curve with the Chebyshev functions. For 2D and 3D imaging, it is found that Lissajous excitation trajectories lead to system functions that are closely related to tensor products of Chebyshev functions.ConclusionSince to date, the MPI system function has to be measured in time-consuming calibration scans, the additional information derived here can be used to reduce the amount of information to be acquired experimentally and can hence speed up system function acquisition. Furthermore, redundancies found in the system function can be removed to arrive at sparser representations that reduce memory load and allow faster image reconstruction.

This paper presents the first detailed simulation approach to evaluate the proposed imaging method called 'magnetic particle imaging' with respect to resolution and sensitivity. The simulated scanner is large enough to accept human bodies. Together with the choice of field strength and noise the setup is representative for clinical applications. Good resolution, fast image acquisition and high sensitivity are demonstrated for various tracer concentrations, acquisition times, tracer properties and fields of view. Scaling laws for the simple prediction of image quality under the variation of these parameters are derived.

Magnetic Particle Imaging (MPI) shows promise for medical imaging, particularly in angiography of patients with chronic kidney disease. As the first biomedical imaging technique that truly depends on nanoscale materials properties, MPI requires highly optimized magnetic nanoparticle tracers to generate quality images. Until now, researchers have relied on tracers optimized for MRI T2*-weighted imaging that are suboptimal for MPI. Here, we describe new tracers tailored to MPI's unique physics, synthesized using an organic-phase process and functionalized to ensure biocompatibility and adequate in vivo circulation time. Tailored tracers showed up to 3x greater SNR and better spatial resolution than existing commercial tracers in MPI images of phantoms.

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.