International Journal on Magnetic Particle Imaging (IJMPI)
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Towards industrial production: An additive approach for magnetic particle spectrometers
No full textMagnetic particle spectroscopy is an innovative method for characterizing magnetic nanoparticles with potentialmedical applications. Traditional methods for instrumentation face challenges such as labor-intensive coilmanufacturing, high costs, high tolerances and setup time. This study introduces a new approach using additivemanufacturing to create rectangular nested coils, enhancing performance and reducing production complexity.The coils, printed with conductive and dielectric inks, allow for modular configurations and improved sensitivity.Results demonstrate a sufficient signal-to-noise ratio and effective cancellation of excitation frequencies. Thispresented technique addresses issues of fine-tuning and manufacturing discrepancies while enabling modular andflexible setups tailored to researchers’ specific needs
Multi-Contrast MPI Matrix Compression
No full textMulti-contrast magnetic particle imaging (MPI) reconstructs the signal from different tracer materials or environments, resulting in multi-channel images that enable temperature or viscosity quantification. Since the multi-contrast problem is ill-posed, it is addressed by regularization methods that are commonly solved using the Kaczmarz algorithm. Unlike the single-contrast MPI problem, the multi-contrast one requires a high number of iterations to converge. Matrix compression techniques were already successfully used in single-contrast reconstruction and matrix recovery applications as in compressed sensing. Our work proposes to use matrix compression to reduce the reconstruction time needed to achieve good reconstruction quality in multi-contrast MPI
Phase-sensitive detection of third-harmonic magnetization signal using magnetoresistive sensor-coupled asymmetric gradiometer
No full textHuman-scale magnetic particle imaging demands a high sensitivity in acquiring weak magnetization signal from nanoparticle tracers. The standard receive chain usually includes high-order filtering, multistage amplification, and broadband noise matching in order to improve signal quality. We incorporate these features into a conventional flux transformer circuit which employs a magnetoresistive sensor coupled with a gradiometric head coil. This approach provides low noise level to map the third harmonic response of a 0.5 mL Ferucarbotran sample under 16 mT/m DC gradient and 1.5 mT/µ0 excitation field at 2 kHz. However, high inductance of asymmetric gradiometer was responsible for a noisy output voltage of an empty bore, limiting the sensitivity up to 1 mgFe. We later adopted lock-in method to differentiate the signal of a few µgFe sample. Although harmonic noises are signal-disruptive for higher excitation fields, this technique recognized a 10 µgFe sample under 2.5 mT/µ0 in the current setup
Measurement of cerebral blood volume modulation in non-human primates
No full textMPI offers a promising alternative to fMRI for detecting changes in cerebral blood volume (CBV) during brain activation, potentially enabling single-patient functional brain mapping. We assess our human-scale MPI brain scanner by imaging anesthetized non-human primates, achieving continuous imaging with 5 s temporal and 7 mm spatial resolution. We successfully detect CBV modulations during alternating cycles of hypercapnia and normocapnia, achieving a CNR of up to 7.9 following activations in the brain region
Efficient Iterative Reconstruction for an MPI Equilibrium Model with Anisotropy
No full textImage reconstruction in Magnetic Particle Imaging (MPI) typically requires a system matrix, obtained through a time-consuming calibration process. To bypass this, various model-based approaches have been explored. Recent work demonstrated successful reconstruction by adapting a Chebyshev approach with Tikhonov-regularized least squares (LS) under an equilibrium model with anisotropy. In this study, we introduce an efficient evaluation of the forward and adjoint operators for the anisotropy model, enabling the use of iterative solvers and alternative regularization methods for image reconstruction
Current-to-Field Prediction for Non-Linear Magnetic Systems via Neural Networks
No full textAccurate magnetic field knowledge is crucial for magnetic particle imaging, affecting performance estimation, sequence generation, and reconstruction. Especially for non-linear field generators, such as those with built-in soft iron, conventional field simulations, such as the finite element method, are computationally demanding. We propose the use of neural networks to predict the coefficients of the spherical harmonic expansions of the fields from the input currents, drastically speeding up current-to-field prediction
Magnetic particle fingerprinting using COMPASS
No full textTo bring the application of magnetic nanoparticles (MNP) closer to application, reliable properties of a MNP suspension need to be ensured for reproducible quality and stability. This aims for a simple, robust and sensitive method that is able to assess the unique characteristics of MNP. We suggest Critical Offset Magnetic Particle SpectroScopy (COMPASS) as a suitable method for these needs, hence it allows for measuring a fingerprint characteristic for a unique MNP suspension. In the following, a dedicated fingerprinting routine to distinguish and classify different magnetic particle types will be introduced.
 
SMART RHESINs for magnetic particle imaging: impact of viscosity-independent relaxation on image reconstruction
No full textMagnetic particle imaging (MPI) is a promising medical imaging modality that leverages the magnetic properties of nanoparticles. Traditionally, MPI tracers have been limited to commercially available nanoparticles, but specialized tracers could improve signal detection and expand applications. Here, we introduce SMART RHESINs, an innovative tracer design for MPI. In SMART RHESINs, synomag®-D nanoparticles are encapsulated in hollow nanospheres, shielding them from external influences like viscosity changes and enabling signal quantification independent of the surrounding medium. We demonstrated that the phase angle obtained through MPS is a rapid, predictive metric for assessing tracer suitability for MPI applications. Unlike the system matrix from non-encapsulated synomag®-D, the system matrix from aqueous synomag®-D-encapsulated SMART RHESINs allowed reconstructing immobilized SMART RHESINs, underscoring design robustness. SMART RHESINs hold potential for quantitative measurements across diverse environments, broadening the scope of MPS and MPI as a versatile tracer platform for quantitative imaging
Magnetic Particle Spectrometry of 3D cancer cell spheroids
No full textSuperparamagnetic iron oxide nanoparticles (SPIONs) have a significant role in biomedical applications suchas hyperthermia therapy or magnetic particle imaging. This study investigates the loading of 3D cell spheroidsmade from a pancreas cell line of rats with magnetic nanoparticles. Here, we show the uptake and a homogeneousdistribution of nanoparticles in spheroids, while magnetic particle spectrometry (MPS) reveals a measurabledifference in the magnetic behavior of nanoparticles in aqueous solution compared to those incorporated intospheroids
Hybrid supraparticles for combined MPI and magnetic hyperthermia
No full textCombining magnetic particle imaging (MPI) with magnetic hyperthermia in a single location opens new effectivetheranostic applications, e.g. in targeted cancer therapy. We present the utilization of hybrid supraparticles con-taining two nanoparticle species to create particles with optimized magnetic properties for both technologies. Forthis, ferromagnetic particles, which are known for their good performance for hyperthermia are combined withsuperparamagnetic particles, which are very well suitable for MPI. The supraparticles are characterized regardingtheir MPS spectra and heating power during magnetic hyperthermia. Additionally, first experiments demonstratethe feasibility of the approach also for imaging. The results represent a proof-of-concept for the supraparticleapproach, which will facilitate the optimization of particle properties for both technnologies at the same time