30 results
Now showing1 - 10 of 30
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Detection and compensation of periodic motion in magnetic particle imaging(Jenny Stanford Publishing, 2026-01-01); ; ; ; The temporal resolution of the tomographic imaging method magnetic particle imaging (MPI) is remarkably high. The spatial resolution is degraded for measured voltage signal with low signal-tonoise ratio, because the regularization in the image reconstruction step needs to be increased for system-matrix approaches and for deconvolution steps in x-space approaches. To improve the signal-tonoise ratio, block-wise averaging of the signal over time can be advantageous. However, since block-wise averaging decreases the temporal resolution, it prevents resolving the motion. In this work, a framework for averaging motion-corrupted MPI raw data is proposed. The motion is considered to be periodic as it is the case for respiration and/or the heart beat. The same state of motion is thus reached repeatedly in a time series exceeding the repetition time of the motion and can be used for averaging. As the motion process and the acquisition process are in general not synchronized, averaging of the captured MPI raw data corresponding to the same state of motion requires to shift the starting point of the individual frames. For high-frequency motion, a higher frame rate is potentially required. To address this issue, a binning method for using only parts of complete frames from a motion cycle is proposed that further reduces the motion artifacts in the final images. The frequency of motion is derived directly from the MPI raw data signal without the need to capture an additional navigator signal. Using a motion phantom it is shown that the proposed method is capable of averaging experimental data with reduced motion artifacts. The methods are further validated on in-vivo data from mouse experiments to compensate the heart beat.Publicationtype: Book Part8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Discriminating nanoparticle core size using multi-contrast MPI(Jenny Stanford Publishing, 2026-01-01); ; ; ; Magnetic particle imaging (MPI) is an imaging modality that detects the response of a distribution of magnetic nanoparticle tracers to alternating magnetic fields. There has recently been exploration into multi-contrast MPI, in which the signal from different tracer materials or environments is separately reconstructed, resulting in multi-channel images that could enable temperature or viscosity quantification. In this work, we apply a multi-contrast reconstruction technique to discriminate between nanoparticle tracers of different core sizes. Three nanoparticle types with core diameters of 21.9 nm, 25.3 nm and 27.7 nm were each imaged at 21 different locations within the scanner field of view. Multi-channel images were reconstructed for each sample and location, with each channel corresponding to one of the three core sizes. For each image, signal weight vectors were calculated, which were then used to classify each image by core size. With a block averaging length of 10 000, the median signal-to-noise ratio was 40 or higher for all three sample types, and a correct prediction rate of 96.7% was achieved, indicating that core size can effectively be predicted using signal weight vector classification with close to 100% accuracy while retaining high MPI image quality. The discrimination of the core size was reliable even when multiple samples of different core sizes were placed in the measuring field.Publicationtype: Book Part4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Uncertainties of a spherical magnetic field cameraSpherical harmonic expansions are well-established tools for estimating magnetic fields from surface measurements and are widely used in applications such as tomographic imaging, geomagnetism, and biomagnetism. Although the mathematical foundations of these expansions are well understood, the impact of real-world imperfections, on the uncertainty of the field model has received little attention. In this work, we present a systematic uncertainty propagation analysis for a magnetic field camera that estimates the field from surface measurements using a spherical array of Hall magnetometers arranged in a spherical t-design. A Monte Carlo-based approach is employed to quantify how sensor-related uncertainties, such as calibration errors and positioning inaccuracies, affect the spatial distribution of the estimated field's uncertainty. The results offer insights into the robustness of spherical harmonic methods and help identify the dominant sources of uncertainty in practical implementations.Publicationtype: Conference Paper29 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Wavelet-based compressed sensing of the system matrix for magnetic particle imagingMagnetic particle imaging (MPI) is a trending tracer imaging technique relatively newly proposed. In MPI, a time-consuming system matrix (SM) calibration scan is required to reconstruct the spatial distribution of superparamagnetic nanoparticles. Compressed sensing (CS) techniques are widely utilized to reduce the time required for SM calibration, with Discrete Cosine Transform (DCT) being the state-of-the-art sparsifying transform. This paper proposes a wavelet-based CS method for SM reconstruction. To the best of our knowledge, this is the first work to employ wavelets as the sparsifying transform for SM recovery in MPI. To this goal, we employ biorthogonal wavelet transform to sparsify the SM rows. We propose a variant of the iterative shrinkage/thresholding-based algorithm for SM reconstruction with the help of bivariate shrinkage to take advantage of the multiscale nature of wavelets and the dependency between wavelet subband coefficients. The performance of the proposed method is assessed by phantom image reconstructions using the state-of-the-art Kaczmarz algorithm. Experimental results using the DCT and the proposed wavelet-based methods confirm that the proposed method outperforms the state-of-the-art DCT-based compression based on the comparisons of (i) the sparsity levels of several SM frequencies, (ii) the reconstruction error of SM frequencies, where the average error for channels 1 and 2 decreased by approximately 10% when using DWT instead of DCT, and (iii) the visual quality of reconstructed phantom images at different sparsity levels, as no ground truth is available for metric-based comparisons.Publicationtype: Journal Article12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Sparse Kaczmarz for convergence speed-up in multi-contrast magnetic particle imagingMagnetic Particle Imaging (MPI) is a tracer-based medical imaging modality with great potential due to its high sensitivity, high spatio-temporal resolution, and capability to quantify the tracer distribution. Image reconstruction in MPI is an ill-posed problem, which regularization methods can address. In MPI, Tikhonov regularization is most commonly used and the corresponding optimization problem is usually solved using the Kaczmarz algorithm. Reconstruction using the Kaczmarz method for single-contrast MPI is very efficient as it produces the desired images fast after a small number of iterations. For multi-contrast MPI, however, the regular Kaczmarz algorithm fails to obtain good-quality images without channel leakage when using a small number of iterations. In this work, we propose a sparsity-promoting regularization term and an associated sparse Kaczmarz method in order to speed up convergence, especially in sparse channels. The proposed method reduces the channel leakage and as a result, speeds up convergence.Publicationtype: Conference Paper22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , On the Receive Path Calibration of Magnetic Particle Imaging SystemsMagnetic nanoparticles are a valuable tool in many biomedical applications and can be used for diagnostic and therapeutic purposes. In magnetic particle imaging (MPI) and magnetic particle spectroscopy (MPS), the particles are subjected to a dynamic magnetic field and the particle magnetization response is simultaneously measured using one or multiple receive coils. Separating the particle signal from the feed-through signal is commonly done by advanced passive filtering, which distorts the particle signal. To correct this distortion, the transfer function of the receive chain needs to be known. While in principle, the transfer function can be simulated, due to imperfections in the electronic components, it is often more accurate to determine the transfer function in a calibration procedure. Although this system calibration, utilizing a calibration-coil setup has been done by several research groups in the past, a general description of the underlying calibration model and methodology is still missing. In this paper we provide a general multi-channel calibration procedure for inductive receive paths in MPI and a blueprint to investigate model and method uncertainties. We generalized the calibration procedure to also cover non-orthogonal and non-homogeneous receive coils. Finally, we showcase the calibration procedure and uncertainty analysis on our custom MPS system and use the MPI transfer functions of misaligned receive coils to decouple their superimposed receive signals from the receive path. The findings enable the comparison of MPI signals from different devices and can be used to normalize measurements and system functions in devices with exchangeable receive coils.Publicationtype: Journal Article35 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Modeling the magnetization dynamics for large ensembles of immobilized magnetic nanoparticles in multi-dimensional magnetic particle imaging(2022-02-01); ; ; ; Magnetic nanoparticles (MNPs) play an important role in biomedical applications including imaging modalities such as magnetic resonance imaging (MRI) and magnetic particle imaging (MPI). The latter one exploits the non-linear magnetization response of a large ensemble of magnetic nanoparticles to magnetic fields which allows determining the spatial distribution of the MNP concentration from measured voltage signals. The image-to-voltage mapping is linear and described by a system matrix. Currently, modeling the voltage signals of large ensembles of MNPs in an MPI environment is not yet accurately possible, especially for liquid tracers in multi-dimensional magnetic excitation fields. As an immediate consequence, the system matrix is still obtained in a time consuming calibration procedure. While the ferrofluidic case can be seen as the typical setting, more recently immobilized and potentially oriented MNPs have received considerable attention. By aligning the particles magnetic easy axis during immobilization one can encode the angle of the particle's magnetic easy axis into the magnetization response providing a relevant benchmark system for model-based approaches. In this work we address the modeling problem for immobilized and oriented MNPs in the context of MPI. We investigate a model-based approach where the magnetization response is simulated by a Néel rotation model for the particle's magnetic moments and the ensemble magnetization is obtained by solving a Fokker–Planck equation approach. Since the parameters of the model are a-priori unknown, we investigate different methods for performing a parameter identification and discuss two different models: One where a single function vector is used from the space spanned by the model parameters and another where a superposition of function vectors is considered. We show that our model can much more accurately reproduce the orientation dependent signal response when compared to the equilibrium model, which marks the current state-of-the-art for model-based system matrix simulations in MPI.Publicationtype: Journal Article28 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Efficient joint estimation of tracer distribution and background signals in magnetic particle imaging using a dictionary approachBackground signals are a primary source of artifacts in magnetic particle imaging and limit the sensitivity of the method since background signals are often not precisely known and vary over time. The state-of-the art method for handling background signals uses one or several background calibration measurements with an empty scanner bore and subtracts a linear combination of these background measurements from the actual particle measurement. This approach yields satisfying results in case that the background measurements are taken in close proximity to the particle measurement and when the background signal drifts linearly. In this work, we propose a joint estimation of particle distribution and background signal based on a dictionary that is capable of representing typical background signals. Reconstruction is performed frame-by-frame with minimal assumptions on the temporal evolution of background signals. Thus, even non-linear temporal evolution of the latter can be captured. Using a singular-value decomposition, the dictionary is derived from a large number of background calibration scans that do not need to be recorded in close proximity to the particle measurement. The dictionary is sufficiently expressive and represented by its principle components. The proposed joint estimation of particle distribution and background signal is expressed as a linear Tikhonov-regularized least squares problem, which can be efficiently solved. In phantom experiments it is shown that the method strongly suppresses background artifacts and even allows to estimate and remove the direct feed-through of the excitation field.Publicationtype: Journal ArticleScopus© Citations 13 49 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Simultaneous imaging of widely differing particle concentrations in MPI: Problem statement and algorithmic proposal for improvement(2021-05-07); ; ; ; Magnetic particle imaging (MPI) is a tomographic imaging technique for determining the spatial distribution of superparamagnetic nanoparticles. Current MPI systems are capable of imaging iron masses over a wide dynamic range of more than four orders of magnitude. In theory, this range could be further increased using adaptive amplifiers, which prevent signal clipping. While this applies to a single sample, the dynamic range is severely limited if several samples with different concentrations or strongly inhomogeneous particle distributions are considered. One scenario that occurs quite frequently in pre-clinical applications is that a highly concentrated tracer bolus in the vascular system 'shadows' nearby organs with lower effective tracer concentrations. The root cause of the problem is the ill-posedness of the MPI imaging operator, which requires regularization for stable reconstruction. In this work, we introduce a simple two-step algorithm that increases the dynamic range by a factor of four. Furthermore, the algorithm enables spatially adaptive regularization, i.e. highly concentrated signals can be reconstructed with maximum spatial resolution, while low concentrated signals are strongly regularized to prevent noise amplification.Publicationtype: Journal Article52 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Dictionary-based background signal estimation for magnetic particle imagingMagnetic Particle Imaging is a highly sensitive tracer-based imaging method able to image as little as a nanogram of tracer material. In practice, background signals stemming from imperfections in the scanner instrumentation make it challenging to achieve these very high sensitivities. In case where the background signal is static over time it can be simply subtracted using an empty reference measurement. In this work we develop a method that is capable of handling dynamic background signals. Since the particle signal and the background signal are superimposed, we propose to perform a joint estimation of both quantities. The background signal space is modeled by a dictionary of background signals, build from a set of representative drifting background measurements. The proposed method is evaluated on experimentally and it is shown that it is capable of accurately estimating the background and tracer signal in a dynamic phantom experiment.Publicationtype: Conference Paper42