Search

Improving resolution and sensitivity will widen possible medical applications of magnetic particle imaging. Pulsed excitation promises such benefits, at the cost of more complex hardware solutions and restrictions on drive field amplitude and frequency. State-of-the-art systems utilize a sinusoidal excitation to drive superparamagnetic nanoparticles into the non-linear part of their magnetization curve, which creates a spectrum with a clear separation of direct feed-through and higher harmonics caused by the particles response. One challenge for rectangular excitation is the discrimination of particle and excitation signals, both broad-band. Another is the drive-field sequence itself, as particles that are not placed at the same spatial position, may react simultaneously and are not separable by their signal phase or shape. To overcome this potential loss of information in spatial encoding for high amplitudes, a superposition of shifting fields and drive-field rotations is proposed in this work. Upon close view, a system matrix approach is capable to maintain resolution, independent of the sequence, if the response to pulsed sequences still encodes information within the phase. Data from an Arbitrary Waveform Magnetic Particle Spectrometer with offsets in two spatial dimensions is measured and calibrated to guarantee device independence. Multiple sequence types and waveforms are compared, based on frequency space image reconstruction from emulated signals, that are derived from measured particle responses. A resolution of 1.0 mT (0.8 mm for a gradient of (−1.25,−1.25,2.5) Tm−1) in x- and y-direction was achieved and a superior sensitivity for pulsed sequences was detected on the basis of reference phantoms. [ABSTRACT FROM AUTHOR]

Magnetic particle imaging is a tracer based imaging technique to determine the spatial distribution of superparamagnetic iron oxide nanoparticles with a high spatial and temporal resolution. Due to physiological constraints, the imaging volume is restricted in size and larger volumes are covered by shifting object and imaging volume relative to each other. This results in reduced temporal resolution, which can lead to motion artifacts when imaging dynamic tracer distributions. A common source of such dynamic distributions are cardiac and respiratory motion in in-vivo experiments, which are in good approximation periodic. We present a raw data processing technique that combines data snippets into virtual frames corresponding to a specific state of the dynamic motion. The technique is evaluated on the basis of measurement data obtained from a rotational phantom at two different rotational frequencies. These frequencies are determined from the raw data without reconstruction and without an additional navigator signal. The reconstructed images give reasonable representations of the rotational phantom frozen in several different states of motion while motion artifacts are suppressed. [ABSTRACT FROM AUTHOR]

The tomographic imaging method magnetic particle imaging (MPI) requires a multi-patch approach for capturing large field of views. This approach consists of a continuous or stepwise spatial shift of a small sub-volume of only few cubic centimeters size, which is scanned using one or multiple excitation fields in the kHz range. Under the assumption of ideal magnetic fields, the MPI system matrix is shift invariant and in turn a single matrix suffices for image reconstruction significantly reducing the calibration time and reconstruction effort. For large field imperfections, however, the method can lead to severe image artifacts. In the present work we generalize the efficient multi-patch reconstruction to work under non-ideal field conditions, where shift invariance holds only approximately for small shifts of the sub-volume. Patches are clustered based on a magnetic-field-based metric such that in each cluster the shift invariance holds in good approximation. The total number of clusters is the main parameter of our method and allows to trade off calibration time and image artifacts. The magnetic-field-based metric allows to perform the clustering without prior knowledge of the system matrices. The developed reconstruction algorithm is evaluated on a multi-patch measurement sequence with 15 patches, where efficient multi-patch reconstruction with a single calibration measurement leads to strong image artifacts. Analysis reveals that calibration measurements can be decreased from 15 to 11 with no visible image artifacts. A further reduction to 9 is possible with only slight degradation in image quality. [ABSTRACT FROM AUTHOR]

Due to peripheral nerve stimulation, the magnetic particle imaging (MPI) method is limited in the maximum applicable excitation-field amplitude. This in turn leads to a limitation of the size of the covered field of view (FoV) to few millimeters. In order to still capture a larger FoV, MPI is capable to rapidly acquire volumes in a multi-patch fashion. To this end, the small excitation volume is shifted through space using the magnetic focus fields. Recently, it has been shown that the individual patches are preferably reconstructed in a joint fashion by solving a single linear system of equations taking the coupling between individual patches into account. While this improves the image quality, it is computationally and memory demanding since the size of the linear system increases in the best case quadratically with the number of patches. In this paper, we will develop a reconstruction algorithm for MPI multi-patch data exploiting the sparsity of the joint system matrix. A highly efficient implicit matrix format allows for rapid on-the-fly calculations of linear algebra operations involving the system matrix. Using this approach, the computational effort can be reduced to a linear dependence on the number of used patches. The algorithm is validated on 3-D multi-patch phantom data sets and shown to reconstruct large data sets with 15 patches in less than 22 s. [ABSTRACT FROM AUTHOR]

Purpose Magnetic particle imaging is a tomographic imaging modality capable of determining the distribution of magnetic nanoparticles with high temporal resolution. The spatial resolution of magnetic particle imaging is influenced by the gradient strength of the selection field used for spatial encoding. By increasing the gradient strength, the spatial resolution is improved, but at the same time the imaging volume decreases. For a high-resolution image of an extended field-of-view, a multipatch approach can be used by shifting the sampling trajectory in space. As the total imaging timescales with the number of patches, the downside of the multipatch method is the degradation of the temporal resolution. Methods The purpose of this work was to develop a scanning procedure incorporating the advantages of imaging at multiple gradient strengths. A low-resolution overview scan is performed at the beginning followed by a small number of high-resolution scans at adaptively detected locations extracted from the low-resolution scan. Results By combining all data during image reconstruction, it is possible to obtain a large field-of-view image of anisotropic spatial resolution. It is measured in a fraction of time compared to a fully sampled high-resolution field of view image. Conclusions Magnetic particle imaging is a flexible imaging method allowing to rapidly scan small volumes. When scaling magnetic particle imaging from small animal to human applications, it will be essential to keep the acquisition time low while still capturing larger volumes at high resolution. With our proposed adaptive multigradient imaging sequence, it is possible to capture a large field of view while keeping both the temporal and the spatial resolution high. [ABSTRACT FROM AUTHOR]

Magnetic Particle Imaging (MPI) is an emerging technology in the field of (pre)clinical imaging. The acquisition of a particle signal is realized along specific sampling trajectories covering a defined field of view (FOV). In a system matrix (SM) based reconstruction procedure, the commonly used acquisition path in MPI is a Lissajous trajectory. Such a trajectory features an inhomogeneous coverage of the FOV, i.e. the center region is sampled less dense than the regions towards the edges of the FOV. Conventionally, the respective SM acquisition and the subsequent reconstruction do not reflect this inhomogeneous coverage. Instead, they are performed on an equispaced grid. The objective of this work is to introduce a sampling grid that inherently features the aforementioned inhomogeneity by using node points of Lissajous trajectories. Paired with a tailored polynomial interpolation of the reconstructed MPI signal, the entire image can be recovered. It is the first time that such a trajectory related non-equispaced grid is used for image reconstruction on simulated and measured MPI data and it is shown that the number of sampling positions can be reduced, while the spatial resolution remains constant. [ABSTRACT FROM PUBLISHER]

In the above article , there is an error in Section III-D regarding the unit of the added noise level on page 1868, second paragraph. It should say “to select a noise level of $n^{r}$ = $70\cdot 10^{-15}$ Am2.” [ABSTRACT FROM AUTHOR]