An arrangement of this kind is known from German patent application DE 101 51 778 A1. In the arrangement described in that publication, first of all a magnetic field having a spatial distribution of the magnetic field strength is generated such that a first sub-zone having a relatively low magnetic field strength and a second sub-zone having a relatively high magnetic field strength are formed in the examination zone. The position in space of the sub-zones in the examination zone is then shifted, so that the magnetization of the particles in the examination zone changes locally. Signals are recorded which are dependent on the magnetization in the examination zone, which magnetization has been influenced by the shift in the position in space of the sub-zones, and information concerning the spatial distribution of the magnetic particles in the examination zone is extracted from these signals, so that an image of the examination zone can be formed. Such an arrangement has the advantage that it can be used to examine arbitrary examination objects—e.g. human bodies—in a non-destructive manner and without causing any damage and with a high spatial resolution, both close to the surface and remote from the surface of the examination object.
A similar arrangement and method is known from Gleich, B. and Weizenecker, J. (2005), “Tomographic imaging using the nonlinear response of magnetic particles” in nature, vol. 435, 30 Jun. 2005, pp. 1214-1217. The arrangement and method for magnetic particle imaging (MPI) described in that publication takes advantage of the non-linear magnetization curve of small magnetic particles.
MPI is a method for imaging distributions of magnetic nano-particles which combines high sensitivity with the ability of fast dynamic imaging, making it a promising candidate for medical imaging applications. MPI applies a new method of signal encoding based on the dynamic displacement of a localized excitation process and allows fast volumetric imaging. However, in contrast to established imaging modalities like MRI and CT, no simple mathematical transform has yet been identified to reconstruct images from the acquired data. Therefore, MPI image reconstruction requires knowledge of a “system function” describing the system response to a given spatial distribution of particles, i.e., mapping particle position to frequency response. To solve the reconstruction problem, the system function has to be inverted, usually requiring some regularization scheme.
The system function can be determined experimentally by measuring the magnetization response of a point-like sample at a large number of spatial positions corresponding to the number of image pixels or voxels. This calibration procedure requires very long acquisition times and furthermore provides a system function that is contaminated with noise. Due to the large size of the system function matrix, solving the inverse reconstruction problem is also quite time-consuming and requires large amounts of computer memory. Also to obtain a reasonable signal-to-noise ratio (SNR) a large acquisition time is required.