International Journal on Magnetic Particle Imaging (IJMPI)
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A refined debye model for the dynamic magnetization response of superparamagnetic nanoparticles
No full textMagnetic Particle Imaging (MPI) uses ferrofluids based on superparamagnetic iron-oxide nanoparticles (SPIONs) asa tracer, whose induced voltage in the receive coils is the measured signal. Image reconstruction is often done withthe system matrix approach, which needs the signal of a test sample at each position for which the concentrationshould be reconstructed. In a scanner with a three dimensional reconstruction volume, this measurement becomesvery time consuming. Functional parameters such as the temperature might also be reconstructed with a systemmatrix approach but would require additional system matrices to be measured. Simulation models for the ferrofluid’smagnetic behavior might be a solution to this problem, if they are sufficiently fast and precise. Here, we comparethe prediction quality and computational cost of the commonly used Debye model with the new Refined Debyemodel on a viscosity measurement
Towards simultaneous imaging of temperature and concentration with single-harmonic-based narrowband MPI
No full textMagnetic Particle Imaging (MPI) is a promising imaging modality for medical applications, which exploits the non-linear magnetization curve of magnetic nanoparticles to visualize the spatial distribution of the tracers. In addition to this diagnostic application, MPI can also be used for therapeutic purposes, like magnetic hyperthermia or targeted drug delivery. Both applications require the knowledge of the temperature of the MNPs and their environment. One opportunity to determine the temperature of MNPs is to detect the phase lag of the particles due to the temperature change. With our single-harmonic-based narrowband MPI system we can measure images of the MNP concentration via the harmonic amplitudes and images of the temperature exploiting the phase of a harmonic. As a next step we work on a combination of both modalities to enable a simultaneous and quantitative concentration and temperature imaging
Smart intratumoral delivery of theranostic gold-iron oxide nanoflowers in prostate cancer using tumor-tropic mesenchymal stem cells
No full textPassive and active targeting of therapeutic nanoparticles (NPs) toward cancer cells has been challenging. New approaches are developed to obtain a more effective targeting method for uniform distribution of NPs within the tumor. We have used tumor-tropic human mesenchymal stem cells (hMSCs) labeled with gold-decorated iron oxide nanoflowers (GIONF) to deliver and retain these theranostic NPs within prostate tumors. GIONF-hMSCs were effective MPI and photothermal therapy (PTT) agents. After i.t. injection in vivo, GIONF-hMSCs remained within tumors for a week without change of MPI signal, while GIONFs alone (“naked” GIONFs) had a ten-fold reduction in MPI signal. We demonstrate the feasibility of imaging GIONF-hMSCs with MPI and CT as a theranostic delivery system for prostate cancer, with improved retention and biodistribution of GIONF-hMSCs compared to naked GIONFs
Adaption of direct Chebyshev reconstruction to an anisotropic particle model
No full textModel-based image reconstruction in magnetic particle imaging (MPI) is an alternative to common reconstruction methods relying on a measured system matrix. It avoids the time-consuming measurement process but has the problem of inferior image quality due to the complex imaging chain that has to be modeled. Recently, a direct reconstruction method using weighted Chebyshev polynomials for multi-dimensional MPI has been proposed that operates in the frequency domain and even does not need a simulated system matrix. However, as the underlying model neglects several physical processes, including the anisotropy of nanoparticles, artifacts in the reconstructed particle distribution can occur.
In this work, an adaption of the direct reconstruction method to an anisotropic particle model is proposed. It is shown that the adaption reduces deformations of the reconstructed particle distribution and thus provides one further step towards fast and high quality model-based image reconstruction
Sample preparations for reproducible measurements in MPS and MPI
No full textMPI is a promising new technology for highly sensitive medical imaging. To acquire reproducible measurements,stable scanner and sample preparations are needed for both system matrix (SM) acquisition and measurement.Therefore, it is important to analyze and subsequently minimize the influence of the scanner hardware, as wellas of the preparation method. In this work, we studied the evolution of the MPI signal of particle samplesusing five sealing methods (tape, hot glue, silicone, silicone + tape, UV glue) in combination with two differenttracers (perimag® and SHP20). In a background examination, we obtained high variations in the signal of theempty scanner over time. For the sample preparation series, we saw that for each type of tracer a differentsealing method worked best. Our results offer insight to sample behavior, which is important for stable SMmeasurements as well as the differentiation of other factors influencing a sample measurement
Investigating the Influence of Sampling Frequency on X-Space MPI Image Reconstructions
No full textIn this presentation we employ a direct X-space deconvolution to estimate particle distributions from MPI data. We report on how the accuracy of those estimations changes as a function of sampling frequency and compare the findings to the MPI core operator approach found in literature
GAN-based deblurring of reconstructed images for MPI
No full textReconstructed images may suffer from blurring and reconstruction artefacts in Magnetic Particle Imaging. In this work, a generative adversarial network is used for deblurring reconstructed images in a post-processing step. The network is trained using eigen-reconstructions of a system matrix and evaluated on synthesized phantoms
Multi-color Kaczmarz Method for Color Magnetic Particle Imaging
No full textColor magnetic particle imaging (cMPI) is an advanced technology to distinguish different kinds of nanoparticles. This technology is realized based on the different harmonic responses and relaxation behavior of particles. However, the commonly used MPI Kaczmarz method has artifacts when it is applied to cMPI reconstruction. To address this problem, a multi-color Kaczmarz method (MKZ) is proposed for cMPI reconstruction. We introduce a new interference item to identify the artifacts caused by the interference between different particles. This method showed better performance than the Kaczmarz method in numerical experiments. It can eliminate the artifacts and efficiently improve the image quality of cMPI
Multi-dimensional Debye model for nanoparticle magnetization in magnetic particle imaging
No full textMagnetic particle imaging (MPI) is a new medical modality to safely and sensitively image the concentration distribution of superparamagnetic iron-oxide nanoparticle (SPIO). It relies on the nonlinear magnetization response of SPIO under time-varying magnetic field to induce output voltage signal. When the multidimensional magnetic fields are applied in MPI, the commonly used first-order Debye model decreases accuracy in modeling the magnetization of SPIO. To solve this problem, we propose a high-order Debye model, which considers the contribution of each dimensional magnetic field on the magnetization of SPIO. Through various experiments, the proposed high-order Debye model shows superiority over the first-order Debye model, with 30% lower root-mean-square error in modeling the magnetization. Using the high-order Debye model, the influence of different magnetic fields on the SPIO can be accurately analyzed, and this model can further provide guidance for MPI instrument optimization
A physics-informed deep learning framework in multi-color magnetic particle signal analysis
No full textMagnetic particle imaging (MPI) is an emerging and highly sensitive imaging method. Multi-color MPI allows simultaneous identification of different materials. Obtaining precise relaxation time is one of the key challenges in achieving multi-colored MPI. In this paper, we propose a physical information based deep learning framework to accurately decompose the mixed signal into the original independent relaxation signals. By transforming the Debye relaxation model into a differential loss function, our network is able to efficiently utilize physical prior information. In simulation experiments with different signal-to-noise ratios and different signal counts, our method shows better performance than the PDCO algorithm. The imaging effect of our algorithm and PDCO algorithm in the presence of multiple materials was evaluated by three-color imaging simulation experiment. In addition, spectral imaging of a digital vascular phantom was simulated by combining a field-free point with homogeneous pulsed excitation. In vascular phantom simulation experiment, our method images blood vessels, metal guidewires, and stents in a single imaging process, showing excellent application potential in cardiac stent surgery