International Journal on Magnetic Particle Imaging (IJMPI)
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Magnetic particle spectroscopy for monitoring the cellular uptake of magnetic nanoparticles: Impact of the excitation field amplitude
No full textMagnetic particle spectroscopy (MPS) is a sensitive method for the quantification of magnetic nanoparticles (MNP). MPS is based on the detection of nonlinear magnetic AC susceptibility and has been established as a rapid and straightforward method for tracer characterization. MPS is also excellent for monitoring the uptake of MNP by cells. The magnetic excitation fields of several millitesla used in MPS measurement raises the possibility of field-induced changes in the MNP sample (e.g., chain formation, aggregation, etc.). In this work, we aim to investigate the field-induced changes in monitoring the uptake of different MNP by THP-1 cells. By using different excitation field amplitudes and measurement scripts, the influence of dynamic magnetic fields on the MPS signal of MNP is investigated and recommendations for continuous MPS measurements of MNP in biological environment are given. We found that the excitation field amplitude and field exposure time can impact the uptake kinetics and influences the temporal resolution. In addition, high SNR can be achieved with a field strength of 12 mT, while at the same time reducing excitation field changes occurring at higher field strengths (such as 25 mT)
Vascular MPI: visualization and tracking of rapidly moving samples
No full textMagnetic Particle Imaging (MPI) is a fast imaging technique for the visualization of the distribution of superparamagnetic iron-oxide nanoparticles (SPIONs) in 3D. For spatial encoding, a field free area is moved rapidly through the field of view (FOV) generating a localized signal. Fast moving samples, e.g., a bolus of SPIONs traveling through the large veins in the human body carried by blood flow with velocities in the order of ~45 cm/s and higher, cause temporal blurring in MPI measurements using common sequences and reconstruction techniques. This hampers the evaluation of dynamics of rapidly moving samples. In this abstract, initial results of rapidly moving samples in form of SPION boluses visualized within an MPI scanner are shown
Relaxation spectral analysis in multi-contrast vascular magnetic particle imaging
No full textMagnetic nanoparticles (MNPs) are used as tracers for vascular imaging without ionizing radiation. There is a high demand for the simultaneous detection of multiple particle states in multi-contrast magnetic particle imaging (MPI). In this study, the Néel and Brownian relaxation times were decoupled and measured separately to characterize different particle states using interventional vascular imaging as an example. The relaxation spectrum was generated via inverse Laplace transform (ILT)-based spectral analysis of the decay signals in the field-flat phase of pulsed excitation. The Néel and Brownian relaxation components were investigated through experiments involving the excitation of synomag-D samples using a trapezoidal-waveform relaxometer. The Brownian relaxation time was identified in the relaxation spectra due to a linear increase with increasing viscosity and disappeared at high gelatin concentrations. The sensitivity of viscosity prediction of the decoupled relaxation times under different excitation-field amplitudes was evaluated. Spectral imaging of a digital vascular phantom was simulated by combining a field-free point with homogeneous pulsed excitation. The plaque region with bounded MNPs and the catheter region with solidified MNPs were simultaneously differentiated from the vessel region in the Brownian relaxation time map. We demonstrated the quantitative assessment of the Néel and Brownian relaxation times through ILT-based spectral analysis in pulsed excitation, highlighting their potential for use in multi-contrast vascular MPI
Dynamic magnetic particle imaging: accurate reconstructions by simultaneous motion estimation
No full textMagnetic Particle Imaging (MPI) has a particularly high spatial and temporal resolution. This temporal resolution makes it a very appropriate candidate for imaging dynamic tracer material and the dynamics itself inside the body. Potential applications include instrument tracking during interventions, but also blood flow imaging, where the dynamics do not only exist but are of high interest as they can be used directly for diagnostic purposes.
However, the image reconstruction task poses a severely ill-posed inverse problem even for static tracer concentrations and we face an even more challenging problem in case of dynamic concentrations. More particularly, we have to accept inaccuracies in our forward modeling in order to obtain fast reconstruction algorithms. Moreover, we expect severe motion artifacts in the reconstructed images due to the non-instantaneous measurements in MPI for two reasons: the motion might exceed one voxel per time frame and the possibility of averaging over frames in order to increase the data SNR is very limited.
In this poster, we propose to solve the dynamic image reconstruction task jointly with motion estimation in between the time frames, as both processes endorse each other and motion estimates are of interest in many dynamic applications [1]. We use different motion models depending on the specific application and start from a fairly general variational problem formulation. The problem is solved by primal-dual splitting using stochastic algorithms, multi-scale approaches and image warping. We present convincing numerical results on measured data.
[1] M. Burger, H. Dirks, and C.-B. Schönlieb. A variational model for joint motion estimation and image reconstruction. SIAM Journal on Imaging Sciences, 11(1):94–128, 2018
A Deep Learning Approach for Automatic Image Reconstruction in MPI
No full textImage reconstruction in magnetic particle imaging is a challenging task because the optimal image quality can only be obtained by tuning the reconstruction parameters for each measurement individually. In particular, it requires a proper selection of the Tikhonov regularization parameter. In this work we propose a deep-learning-based post-processing technique, which removes the need for manual parameter optimization. The proposed neural network takes several images reconstructed with different parameters as input and combines them into a single high-quality image
Deep Generative Adversarial Networks for Direct Super-resolution Magnetic Particle Imaging Reconstruction
No full textDue to the complex physical behavior of the nanoparticles, it is challenging to reconstruct the image from the magnetic particle signal. Since the system matrix reconstruction is time-consuming and the x-space reconstruction ignores the relaxation effect of the particles, we proposed to reconstruct the high-resolution 2-D image directly from the 1-D magnetic particle voltage signal by using the machine learning method of generative adversarial network (GAN). We first built a large simulation image dataset, which includes 291,597 binary images and each image’s corresponding MPI voltage signal simulated with our developed MPI simulation software MPIRF. By using the large simulation dataset, we trained a conditional-GAN model, which we termed “MPIGAN”, that can successfully convert the 1-D MPI voltage signal to the high-resolution MPI image directly and efficiently. Experiment results showed that, compared to the traditional methods, our proposed MPIGAN could better retrieve the fine-scale structure of the patterns of images from the 1-D voltage signals, and achieved better reconstruction performance in both visual effects and quantitative assessments, e.g., SSIM, MSE, PSNR. Our study provides a promising end-to-end AI solution for the efficient and high-resolution magnetic particle imaging reconstruction
Brownian superparamagnetic nanoparticles for cell viability assessment in Magnetic Particle Imaging
No full textMolecular imaging tools can noninvasively track cells in vivo. However, no techniques today can rapidly monitor cell therapies to allow for nimble treatment optimization for each patient, the epitome of Personalized Medicine. Magnetic Particle Imaging (MPI) is a new tracer imaging technology that could soon provide MDs unequivocal therapy treatment feedback in just three days. MPI with Brownian superparamagnetic iron oxide nanoparticles shows promise towards noninvasive sensing of cell viability via viscosity changes in apoptotic cells. This unique ability could greatly improve the efficacy of cell therapies by enabling rapid personalization of the treatment
Estimation of hydrodynamic size distribution of magnetic nanoparticles based on AC magnetization harmonics for magnetic immunoassay
No full textMagnetic nanoparticles (MNPs) have been widely studied for use in biomedical applications such as magnetic immunoassay. The hydrodynamic size distribution is an important physical characteristic for estimating the binding behavior in magnetic immunoassay. In this study, we proposed a method to estimate the hydrodynamic size distribution of MNPs based on the AC magnetization harmonics. The matrix equation of hydrodynamic size distribution was constructed based on the Fokker-Planck equation dominated by Brownian relaxation, and inversed by a Tikhonov regularization least squares algorithm. The simulation results show that the proposed method can accurately estimate the hydrodynamic size distribution, which is expected to useful for biosensor applications
Self-supervised signal denoising in magnetic particle imaging
No full textVarious noises restrict magnetic particle imaging (MPI) to achieve higher resolution and sensitivity in practice. In this study,we proposed a self-supervised learning method to denoise MPI signals. The deep learning-based architecture consisted with fourencoder’s blocks (EcBs) and four decoder’s blocks (DcBs). This model was trained with limited data of MPI magnetization signals to efficiently suppress noise related features by directly learning from the noisy signals. Simulated experiments showed that the selfsupervised method could reduce the noise interference in MPI signals and eventually improve image qualit
Core size analysis of magnetic nanoparticles using frequency mixing magnetic detection with a permanent magnet as an offset source
No full textFrequency mixing magnetic detection (FMMD) has been widely used in magnetic immunoassay measurement techniques. It can also be used to characterize and distinguish different magnetic nanoparticle (MNP) types according to their magnetic cores size. In a previous work, a method for resolving ambiguities in determination of the core size distribution was utilized involving measurement of total iron mass. Recently, a new FMMD measurement head was developed in which a pair of permanent ring magnets are used to generate the static offset magnetic field. Here, we show that this new measurement head can be applied for determining the core size distribution of MNP, and compare the results with the outcomes of our conventional electromagnet offset module FMMD