Weighted iterative reconstruction for magnetic particle imaging

SPFS: SNR peak-based frequency selection method to alleviate resolution degradation in MPI real-time imaging

Objective. The primary objective of this study is to address the reconstruction time challenge in magnetic particle imaging (MPI) by introducing a novel approach named SNR-peak-based frequency selection (SPFS). The focus is on improving spatial resolution without compromising reconstruction speed, thereby enhancing the clinical potential of MPI for real-time imaging.Approach. To overcome the trade-off between reconstruction time and spatial resolution in MPI, the researchers propose SPFS as an innovative frequency selection method. Unlike conventional SNR-based selection, SPFS prioritizes frequencies with signal-to-noise ratio (SNR) peaks that capture crucial system matrix information. This adaptability to varying quantities of selected frequencies enhances versatility in the reconstruction process. The study compares the spatial resolution of MPI reconstruction using both SNR-based and SPFS frequency selection methods, utilizing simulated and real device data.Main results.The research findings demonstrate that the SPFS approach substantially improves image resolution in MPI, especially when dealing with a limited number of frequency components. By focusing on SNR peaks associated with critical system matrix information, SPFS mitigates the spatial resolution degradation observed in conventional SNR-based selection methods. The study validates the effectiveness of SPFS through the assessment of MPI reconstruction spatial resolution using both simulated and real device data, highlighting its potential to address a critical limitation in the field.Significance.The introduction of SPFS represents a significant breakthrough in MPI technology. The method not only accelerates reconstruction time but also enhances spatial resolution, thus expanding the clinical potential of MPI for various applications. The improved real-time imaging capabilities of MPI, facilitated by SPFS, hold promise for advancements in drug delivery, plaque assessment, tumor treatment, cerebral perfusion evaluation, immunotherapy guidance, andin vivocell tracking.

Combination of time domain-system matrix and x-space methods to reconstruct magnetic particle images with isotropic resolution

Objective. Imaging of superparamagnetic iron oxide nanoparticles based on their non-linear response to alternating magnetic fields shows promise for imaging cells and vasculature in healthy and diseased tissue. Such imaging can be achieved through x-space reconstruction typically along a unidirectional Cartesian trajectory, which rapidly convolutes the particle distribution with a 'anisotropic blurring' point spread function (PSF), leading to images with anisotropic resolution.Approach. Here we propose combining the time domine-system matrix and x-space reconstruction methods into a forward model, where the output of the forward model is the PSF-blurred x-space reconstructed image. We then treat the blur as an inverse problem solved by Kaczmarz iteration.Main results. After we have proposed the method optimization, the normal resolution of simulation and device images has been increased from 3.5 mm and 5.25 mm to 1.5 mm and 3.25 mm, which has reached the level in the tangential resolution. Quantitative indicators of image quality such as PSNR and SSIM have also been greatly improved.Significance. Simulation and imaging of real phantoms indicate that our approach provides better isotropic resolution and image quality than the x-space method alone or other methods for removing PSF blur. Using our proposed method to optimize the image quality of x-space reconstructed images using unidirectional Cartesian trajectories, it will promote the clinical application of MPI in the future.

Real-time multi-contrast magnetic particle imaging for the detection of gastrointestinal bleeding

Gastrointestinal bleeding, as a potentially life-threatening condition, is typically diagnosed by radiation-based imaging modalities like computed tomography or more invasively catheter-based angiography. Endoscopy enables examination of the upper gastrointestinal tract and the colon but not of the entire small bowel. Magnetic Particle Imaging (MPI) enables non-invasive, volumetric imaging without ionizing radiation. The aim of this study was to evaluate the feasibility of detecting gastrointestinal bleeding by single- and multi-contrast MPI using human-sized organs. A 3D-printed small bowel phantom and porcine small bowel specimens were prepared with a defect within the bowel wall as the source of a bleeding. For multi-contrast MPI, the bowel lumen was filled with an intestinal tracer representing an orally administered tracer. MPI was performed to evaluate the fluid exchange between the vascular compartment of the bowel wall and the lumen while a blood pool tracer was applied. Leakage of the blood pool tracer was observed to the bowel lumen. Multi-contrast MPI enabled co-registration of both tracers at the same location within the bowel lumen indicating gastrointestinal bleeding. Single- and multi-contrast MPI are feasible to visualize gastrointestinal bleeding. Therefore, MPI might emerge as a useful tool for radiation-free detection of bleeding within the entire gastrointestinal tract.

DERnet: a deep neural network for end-to-end reconstruction in magnetic particle imaging

Objective. Magnetic particle imaging (MPI) shows potential for contributing to biomedical research and clinical practice. However, MPI images are effectively affected by noise in the signal as its reconstruction is an ill-posed inverse problem. Thus, effective reconstruction method is required to reduce the impact of the noise while mapping signals to MPI images. Traditional methods rely on the hand-crafted data-consistency (DC) term and regularization term based on spatial priors to achieve noise-reducing and reconstruction. While these methods alleviate the ill-posedness and reduce noise effects, they may be difficult to fully capture spatial features.Approach. In this study, we propose a deep neural network for end-to-end reconstruction (DERnet) in MPI that emulates the DC term and regularization term using the feature mapping subnetwork and post-processing subnetwork, respectively, but in a data-driven manner. By doing so, DERnet can better capture signal and spatial features without relying on hand-crafted priors and strategies, thereby effectively reducing noise interference and achieving superior reconstruction quality.Main results. Our data-driven method outperforms the state-of-the-art algorithms with an improvement of 0.9-8.8 dB in terms of peak signal-to-noise ratio under various noise levels. The result demonstrates the advantages of our approach in suppressing noise interference. Furthermore, DERnet can be employed for measured data reconstruction with improved fidelity and reduced noise. In conclusion, our proposed method offers performance benefits in reducing noise interference and enhancing reconstruction quality by effectively capturing signal and spatial features.Significance. DERnet is a promising candidate method to improve MPI reconstruction performance and facilitate its more in-depth biomedical application.

An adaptive multi-frame parallel iterative method for accelerating real-time magnetic particle imaging reconstruction

Objective. Real-time reconstruction of magnetic particle imaging (MPI) shows promising clinical applications. However, prevalent reconstruction methods are mainly based on serial iteration, which causes large delay in real-time reconstruction. In order to achieve lower latency in real-time MPI reconstruction, we propose a parallel method for accelerating the speed of reconstruction methods.Approach. The proposed method, named adaptive multi-frame parallel iterative method (AMPIM), enables the processing of multi-frame signals to multi-frame MPI images in parallel. To facilitate parallel computing, we further propose an acceleration strategy for parallel computation to improve the computational efficiency of our AMPIM.Main results. OpenMPIData was used to evaluate our AMPIM, and the results show that our AMPIM improves the reconstruction frame rate per second of real-time MPI reconstruction by two orders of magnitude compared to prevalent iterative algorithms including the Kaczmarz algorithm, the conjugate gradient normal residual algorithm, and the alternating direction method of multipliers algorithm. The reconstructed image using AMPIM has high contrast-to-noise with reducing artifacts.Significance. The AMPIM can parallelly optimize least squares problems with multiple right-hand sides by exploiting the dimension of the right-hand side. AMPIM has great potential for application in real-time MPI imaging with high imaging frame rate.

Determining the resolution of a tracer for magnetic particle imaging by means of magnetic particle spectroscopy

Magnetic particle imaging (MPI) is an imaging modality to quantitatively determine the three-dimensional distribution of magnetic nanoparticles (MNPs) administered as a tracer into a biological system. Magnetic particle spectroscopy (MPS) is the zero-dimensional MPI counterpart without spatial coding but with much higher sensitivity. Generally, MPS is employed to qualitatively evaluate the MPI capability of tracer systems from the measured specific harmonic spectra. Here, we investigated the correlation of three characteristic MPS parameters with the achievable MPI resolution from a recently introduced procedure based on a two-voxel-analysis of data taken from the system function acquisition that is mandatory in Lissajous scanning MPI. We evaluated nine different tracer systems and determined their MPI capability and resolution from MPS measurements and compared the results with MPI phantom measurements.

System matrix recovery based on deep image prior in magnetic particle imaging

Objective.Magnetic particle imaging (MPI) is an emerging tomography imaging technique with high specificity and temporal-spatial resolution. MPI reconstruction based on the system matrix (SM) is an important research content in MPI. However, SM is usually obtained by measuring the response of an MPI scanner at all positions in the field of view. This process is very time-consuming, and the scanner will overheat in a long period of continuous operation, which is easy to generate thermal noise and affects MPI imaging performance.Approach.In this study, we propose a deep image prior-based method that prominently decreases the time of SM calibration. It is an unsupervised method that utilizes the neural network structure itself to recover a high-resolution SM from a downsampled SM without the need to train the network using a large amount of training data.Main results.Experiments on the Open MPI data show that the time of SM calibration can be greatly reduced with only slight degradation of image quality.Significance.This study provides a novel method for obtaining SM in MPI, which shows the potential to achieve SM recovery at a high downsampling rate. It is expected that this study will increase the practicability of MPI in biomedical applications and promote the development of MPI in the future.

Weighted sum of harmonic signals for direct imaging in magnetic particle imaging

Objective.Magnetic particle imaging (MPI) is a novel radiation-free medical imaging modality that can directly image superparamagnetic iron oxide tracers (SPIOs) with high sensitivity, temporal resolution, and good spatial resolution. The MPI reconstruction task can be formulated mathematically as a Fredholm integral problem, but the concrete inversion is not easily possible because of the particle dynamics or scanner issues. Measurement based system matrix inversion takes these factors into account, but prior measurement and calibration are time consuming.Approach.We proposed a direct imaging method based on the weighted sum of harmonic signals. The harmonic signals with spatial information are obtained by the short-time Fourier transform, and odd harmonic components are selected for recombination and then mapped to the sampling trajectory to image the concentration distribution of SPIOs. In addition, we adopt a normalized-weighted sum of harmonics to improve the resolution of the native image.Main results.The effectiveness of the proposed method is verified by simulation imaging experiments and our in-house scanner-based experiments. Quantitative evaluation results show that compared with traditional methods, the structural similarity improved by 48%, mean square error decreased by 88%, and signal-to-artifact ratio increased by 2.5 times.Significance.The proposed method can rapidly image the concentration distribution of nanoparticles without any prior calibration measurements and reduce the blur of MPI images without deconvolution, which has the potential to be implemented as a multi-patch imaging method in MPI.

Recent developments of the reconstruction in magnetic particle imaging

Magnetic particle imaging (MPI) is an emerging molecular imaging technique with high sensitivity and temporal-spatial resolution. Image reconstruction is an important research topic in MPI, which converts an induced voltage signal into the image of superparamagnetic iron oxide particles concentration distribution. MPI reconstruction primarily involves system matrix- and x-space-based methods. In this review, we provide a detailed overview of the research status and future research trends of these two methods. In addition, we review the application of deep learning methods in MPI reconstruction and the current open sources of MPI. Finally, research opinions on MPI reconstruction are presented. We hope this review promotes the use of MPI in clinical applications.

Adaptive permissible region based random Kaczmarz reconstruction method for localization of carotid atherosclerotic plaques in fluorescence molecular tomography

Objective. In this study, we propose the adaptive permissible region based random Kaczmarz method as an improved reconstruction method to recover small carotid atherosclerotic plaque targets in rodents with high resolution in fluorescence molecular tomography (FMT). Approach. We introduce the random Kaczmarz method as an advanced minimization method to solve the FMT inverse problem. To satisfy the special condition of this method, we proposed an adaptive permissible region strategy based on traditional permissible region methods to flexibly compress the dimension of the solution space. Main results. Monte Carlo simulations, phantom experiments, and in vivo experiments demonstrate that the proposed method can recover the small carotid atherosclerotic plaque targets with high resolution and accuracy, and can achieve lower root mean squared error and distance error (DE) than other traditional methods. For targets with 1.5 mm diameter and 0.5 mm separation, the DE indicators can be improved by up to 40%. Moreover, the proposed method can be utilized for in vivo locating atherosclerotic plaques with high accuracy and robustness. Significance. We applied the random Kaczmarz method to solve the inverse problem in FMT and improve the reconstruction result via this advanced minimization method. We verified that the FMT technology has a great potential to locate and quantify atherosclerotic plaques with higher accuracy, and can be expanded to more preclinical research.

System Matrix Based Reconstruction for Pulsed Sequences in Magnetic Particle Imaging

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.

Bimodal Interventional Instrument Markers for Magnetic Particle Imaging and Magnetic Resonance Imaging—A Proof-of-Concept Study

The purpose of this work was to develop instrument markers that are visible in both magnetic particle imaging (MPI) and magnetic resonance imaging (MRI). The instrument markers were based on two different magnetic nanoparticle types (synthesized in-house KLB and commercial Bayoxide E8706). Coatings containing one of both particle types were fabricated and measured with a magnetic particle spectrometer (MPS) to estimate their MPI performance. Coatings based on both particle types were then applied on a segment of a nonmetallic guidewire. Imaging experiments were conducted using a commercial, preclinical MPI scanner and a preclinical 1 tesla MRI system. MPI image reconstruction was performed based on system matrices measured with dried KLB and Bayoxide E8706 coatings. The bimodal markers were clearly visible in both methods. They caused circular signal voids in MRI and areas of high signal intensity in MPI. Both the signal voids as well as the areas of high signal intensity were larger than the real marker size. Images that were reconstructed with a Bayoxide E8706 system matrix did not show sufficient MPI signal. Instrument markers with bimodal visibility are essential for the perspective of monitoring cardiovascular interventions with MPI/MRI hybrid systems.

Frequency-selective signal enhancement by a passive dual coil resonator for magnetic particle imaging

Objective.Magnetic particle imaging (MPI) visualizes the spatial distribution of magnetic nanoparticles. MPI already provides excellent temporal and good spatial resolution, however, to achieve translation into clinics, further advances in the fields of sensitivity, image reconstruction and tracer performance are needed. In this work, we propose a novel concept to enhance the MPI signal and image resolution by a purely passive receive coil insert for a preclinical MPI system.Approach.The passive dual coil resonator (pDCR) provides frequency-selective signal enhancement. This is enabled by the adaptable resonance frequency of the pDCR network, which is galvanically isolated from the MPI system and composed of two coaxial solenoids connected via a capacitor. The pDCR aims to enhance frequency components related to high mixing orders, which are crucial to achieve high spatial resolution.Main Results.In this study, system matrix measurements and image acquisitions of a resolution phantom are carried out to evaluate the performance of the pDCR compared to the integrated receive unit of the preclinical MPI and a dedicated rat-sized receive coil. Frequency-selective signal increase and spatial resolution enhancement are demonstrated.Significance.Common dedicated receive coils come along with noise-matched receive networks, which makes them costly and difficult to reproduce. The presented pDCR is a purely passive coil insert that gets along without any additional receive electronics. Therefore, it is cost-efficient, easy-to-handle and adaptable to other MPI scanners and potentially other applications providing the basis for a new breed of passive MPI receiver systems.

Direct multi-dimensional Chebyshev polynomial based reconstruction for magnetic particle imaging

Magnetic Particle Imaging is a tomographic imaging technique that measures the voltage induced due to magnetization changes of magnetic nanoparticle distributions. The relationship between the received signal and the distribution of the nanoparticels is described by the system function. A common method for image reconstruction is using a measured system function to create a system matrix and set up a regularized linear system of equations. Since the measurement of the system matrix is time-consuming, different methods for acceleration have been proposed. These include modeling the system matrix or using a direct reconstruction method in time, known as X-space reconstruction. In this work, based on the simplified Langevin model of paramagnetism and certain approximations, a direct reconstruction technique for Magnetic Particle Imaging in the frequency domain with two- and three-dimensional Lissajous trajectory excitation is presented. The approach uses Chebyshev polynomials of second kind. During reconstruction, they are weighted with the frequency components of the voltage signal and additional factors and then summed up. To obtain the final nanoparticle distribution, this result is rescaled and deconvolved. It is shown that the approach works for both simulated data and real measurements. The obtained image quality is comparable to a modeled system matrix approach using the same simplified physical assumptions and no relaxation effects. The reconstruction of a 31 × 31 × 31 volume takes less than a second and is up to 25 times faster than the state-of-the-art Kaczmarz reconstruction. Besides, the derivation of the proposed method shows some new theoretical aspects of the system function and its well-known observed similarity to tensor products of Chebyshev polynomials of second kind.

Efficient Joint Estimation of Tracer Distribution and Background Signals in Magnetic Particle Imaging Using a Dictionary Approach

Background 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.

Magnetic particle imaging for assessment of cerebral perfusion and ischemia

Stroke is one of the leading worldwide causes of death and sustained disability. Rapid and accurate assessment of cerebral perfusion is essential to diagnose and successfully treat stroke patients. Magnetic particle imaging (MPI) is a new technology with the potential to overcome some limitations of established imaging modalities. It is an innovative and radiation-free imaging technique with high sensitivity, specificity, and superior temporal resolution. MPI enables imaging and diagnosis of stroke and other neurological pathologies such as hemorrhage, tumors, and inflammatory processes. MPI scanners also offer the potential for targeted therapies of these diseases. Due to lower field requirements, MPI scanners can be designed as resistive magnets and employed as mobile devices for bedside imaging. With these advantages, MPI could accelerate and improve the diagnosis and treatment of neurological disorders. This review provides a basic introduction to MPI, discusses its current use for stroke imaging, and addresses future applications, including the potential for clinical implementation. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.

Fast MPI reconstruction with non-smooth priors by stochastic optimization and data-driven splitting

Magnetic particle images are currently most often reconstructed using classical Tikhonov regularization (i.e. anℓ²regularization term) combined with Kaczmarz method. Quality enhancing choices like sparsity promotingℓ¹-regularization or TV regularization lead to problems that cannot be solved by standard Kaczmarz method. We propose to use stochastic primal-dual hybrid gradient method to gain more flexibility concerning the choice of data fitting term and regularization, respectively, and still obtain an algorithm which is at least as fast as Kaczmarz method. The proposed algorithm performs comparably to the current state-of-the-art method in terms of run time. The quality of reconstructions can be significantly improved as different regularization terms can be easily integrated. Moreover, in order to achieve further speed up of the method, we propose two new step size rules which lead to fast convergence and make the algorithm very easy to handle. We improve the performance of the algorithm further by applying a data-driven splitting scheme leading to a significant speed-up during the first iterations.

A wavelet-based sparse row-action method for image reconstruction in magnetic particle imaging

PURPOSE

Magnetic particle imaging (MPI) is a preclinical imaging technique capable of visualizing the spatio-temporal distribution of magnetic nanoparticles. The image reconstruction of this fast and dynamic process relies on efficiently solving an ill-posed inverse problem. Current approaches to reconstruct the tracer concentration from its measurements are either adapted to image characteristics of MPI but suffer from higher computational complexity and slower convergence or are fast but lack in the image quality of the reconstructed images.

METHODS

In this work we propose a novel MPI reconstruction method to combine the advantages of both approaches into a single algorithm. The underlying sparsity prior is based on an undecimated wavelet transform and is integrated into a fast row-action framework to solve the corresponding MPI minimization problem.

RESULTS

Its performance is numerically evaluated against a classical FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) approach on simulated and real MPI data. The experimental results show that the proposed method increases image quality with significantly reduced computation times.

CONCLUSIONS

In comparison to state-of-the-art MPI reconstruction methods, our approach shows better reconstruction results and at the same time accelerates the convergence rate of the underlying row-action algorithm.

The Reconstruction of Magnetic Particle Imaging: Current Approaches Based on the System Matrix

Magnetic particle imaging (MPI) is a novel non-invasive molecular imaging technology that images the distribution of superparamagnetic iron oxide nanoparticles (SPIONs). It is not affected by imaging depth, with high sensitivity, high resolution, and no radiation. The MPI reconstruction with high precision and high quality is of enormous practical importance, and many studies have been conducted to improve the reconstruction accuracy and quality. MPI reconstruction based on the system matrix (SM) is an important part of MPI reconstruction. In this review, the principle of MPI, current construction methods of SM and the theory of SM-based MPI are discussed. For SM-based approaches, MPI reconstruction mainly has the following problems: the reconstruction problem is an inverse and ill-posed problem, the complex background signals seriously affect the reconstruction results, the field of view cannot cover the entire object, and the available 3D datasets are of relatively large volume. In this review, we compared and grouped different studies on the above issues, including SM-based MPI reconstruction based on the state-of-the-art Tikhonov regularization, SM-based MPI reconstruction based on the improved methods, SM-based MPI reconstruction methods to subtract the background signal, SM-based MPI reconstruction approaches to expand the spatial coverage, and matrix transformations to accelerate SM-based MPI reconstruction. In addition, the current phantoms and performance indicators used for SM-based reconstruction are listed. Finally, certain research suggestions for MPI reconstruction are proposed, expecting that this review will provide a certain reference for researchers in MPI reconstruction and will promote the future applications of MPI in clinical medicine.

Simultaneous imaging of widely differing particle concentrations in MPI: problem statement and algorithmic proposal for improvement

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.

Magnetic Particle Imaging Reconstruction Based on the Least Absolute Shrinkage and Selection Operator Regularization

Magnetic particle imaging is a new medical imaging modality which is based on the non-linear response of magnetic nanoparticles. The reconstruction task is an inverse problem and ill-posed in nature. To overcome the problem, we propose to use the least absolute shrinkage and selection operator (LASSO) regularization model. In order to reach a good result with a short reconstruction time, we use the truncated system matrix and the truncated measurement based on two threshold setting methods for reconstruction research. In this paper, we study the reconstruction quality of different threshold values and different regularization parameter values. We compare the reconstruction performance of the proposed model with the Tikhonov model from visualization and performance indicators. The conducted study illustrated that the proposed method yields significantly higher reconstruction quality than the state-of-the-art reconstruction method based on Tikhonov model.

Magnetic particle imaging of particle dynamics in complex matrix systems

Magnetic particle imaging (MPI) is a young imaging modality for biomedical applications. It uses magnetic nanoparticles as a tracer material to produce three-dimensional images of the spatial tracer distribution in the field-of-view. Since the tracer magnetization dynamics are tied to the hydrodynamic mobility via the Brownian relaxation mechanism, MPI is also capable of mapping the local environment during the imaging process. Since the influence of viscosity or temperature on the harmonic spectrum is very complicated, we used magnetic particle spectroscopy (MPS) as an integral measurement technique to investigate the relationships. We studied MPS spectra as function of both viscosity and temperature on model particle systems. With multispectral MPS, we also developed an empirical tool for treating more complex scenarios via a calibration approach. We demonstrate that MPS/MPI are powerful methods for studying particle-matrix interactions in complex media.

Tomographic Field Free Line Magnetic Particle Imaging With an Open-Sided Scanner Configuration

Superparamagnetic iron oxide nanoparticles (SPIONs) have a high potential for use in clinical diagnostic and therapeutic applications. In vivo distribution of SPIONs can be imaged with the Magnetic Particle Imaging (MPI) method, which uses an inhomogeneous magnetic field with a field free region (FFR). The spatial distribution of the SPIONs are obtained by scanning the FFR inside the field of view (FOV) and sensing SPION related magnetic field disturbance. MPI magnets can be configured to generate a field free point (FFP), or a field free line (FFL) to scan the FOV. FFL scanners provide more sensitivity, and are also more suitable for scanning large regions compared to FFP scanners. Interventional procedures will benefit greatly from FFL based open magnet configurations. Here, we present the first open-sided MPI system that can electronically scan the FOV with an FFL to generate tomographic MPI images. Magnetic field measurements show that FFL can be rotated electronically in the horizontal plane and translated in three dimensions to generate 3D MPI images. Using the developed scanner, we obtained 2D images of dot and cylinder phantoms with varying iron concentrations between 11 [Formula: see text]/ml and 770 [Formula: see text]/ml. We used a measurement based system matrix image reconstruction method that minimizes l₁ -norm and total variation in the images. Furthermore, we present 2D imaging results of two 4 mm-diameter vessel phantoms with 0% and 75% stenosis. The experiments show high quality imaging results with a resolution down to 2.5 mm for a relatively low gradient field of 0.6 T/m.

Implementation method for magneto-acoustic concentration tomography with magnetic induction (MACT-MI) based on the method of moments

Magneto-acoustic concentration tomography of magnetic nanoparticles with magnetic induction (MACT-MI) is a new imaging technology that combines the advantages of the high sensitivity of magnetic particle imaging and the high resolution of ultrasonic imaging. This technique has broad application prospects in the biomedical and molecular imaging fields. In this study, a reconstruction algorithm based on the method of moments (MoM) is proposed for the MACT-MI inverse problem. Image reconstructions of the acoustic source and superparamagnetic nanoparticle (SPN) concentration were performed using different shape models, and the reconstructed images were analyzed. In addition, the effect of the radius of the tissue region loaded with SPNs on the quality of the reconstructed images was evaluated. The results demonstrated that the new method could reconstruct the SPN concentration distribution well, and a negative correlation existed between the radius of the imaging model and reconstructed image quality. The finding of this research can potentially contribute to the development of MACT-MI in medicine.

Suppression of Motion Artifacts Caused by Temporally Recurring Tracer Distributions in Multi-Patch Magnetic Particle Imaging

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.

Visualization of spatial and temporal temperature distributions with magnetic particle imaging for liver tumor ablation therapy

Temperature-resolved magnetic particle imaging (MPI) represents a promising tool for medical imaging applications. In this study an approach based on a single calibration measurement was applied for highlighting the potential of MPI for monitoring of temperatures during thermal ablation of liver tumors. For this purpose, liver tissue and liver tumor phantoms embedding different superparamagnetic iron oxide nanoparticles (SPION) were prepared, locally heated up to 70 °C and recorded with MPI. Optimal temperature MPI SPIONs and a corresponding linear model for temperature calculation were determined. The temporal and spatial temperature distributions were compared with infrared (IR) camera results yielding quantitative agreements with a mean absolute deviation of 1 °C despite mismatches in boundary areas.

Generalized MPI Multi-Patch Reconstruction Using Clusters of Similar System Matrices

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.

In-Vitro MPI-guided IVOCT catheter tracking in real time for motion artifact compensation

PURPOSE

Using 4D magnetic particle imaging (MPI), intravascular optical coherence tomography (IVOCT) catheters are tracked in real time in order to compensate for image artifacts related to relative motion. Our approach demonstrates the feasibility for bimodal IVOCT and MPI in-vitro experiments.

MATERIAL AND METHODS

During IVOCT imaging of a stenosis phantom the catheter is tracked using MPI. A 4D trajectory of the catheter tip is determined from the MPI data using center of mass sub-voxel strategies. A custom built IVOCT imaging adapter is used to perform different catheter motion profiles: no motion artifacts, motion artifacts due to catheter bending, and heart beat motion artifacts. Two IVOCT volume reconstruction methods are compared qualitatively and quantitatively using the DICE metric and the known stenosis length.

RESULTS

The MPI-tracked trajectory of the IVOCT catheter is validated in multiple repeated measurements calculating the absolute mean error and standard deviation. Both volume reconstruction methods are compared and analyzed whether they are capable of compensating the motion artifacts. The novel approach of MPI-guided catheter tracking corrects motion artifacts leading to a DICE coefficient with a minimum of 86% in comparison to 58% for a standard reconstruction approach.

CONCLUSIONS

IVOCT catheter tracking with MPI in real time is an auspicious method for radiation free MPI-guided IVOCT interventions. The combination of MPI and IVOCT can help to reduce motion artifacts due to catheter bending and heart beat for optimized IVOCT volume reconstructions.

Fast System Calibration With Coded Calibration Scenes for Magnetic Particle Imaging

Magnetic particle imaging (MPI) is a relatively new medical imaging modality, which detects the nonlinear response of magnetic nanoparticles (MNPs) that are exposed to external magnetic fields. The system matrix (SM) method for MPI image reconstruction requires a time consuming system calibration scan prior to image acquisition, where a single MNP sample is measured at each voxel position in the field-of-view (FOV). The scanned sample has the maximum size of a voxel so that the calibration measurements have relatively poor signal-to-noise ratio (SNR). In this paper, we present the coded calibration scene (CCS) framework, where we place multiple MNP samples inside the FOV in a random or pseudo-random fashion. Taking advantage of the sparsity of the SM, we reconstruct the SM by solving a convex optimization problem with alternating direction method of multipliers using CCS measurements. We analyze the effects of filling rate, number of measurements, and SNR on the SM reconstruction using simulations and demonstrate different implementations of CCS for practical realization. We also compare the imaging performance of the proposed framework with that of a standard compressed sensing SM reconstruction that utilizes a subset of calibration measurements from a single MNP sample. The results show that CCS significantly reduces calibration time while increasing both the SM reconstruction and image reconstruction performances.

Fully automated gridding reconstruction for non-Cartesian x-space magnetic particle imaging

Magnetic particle imaging (MPI) is a fast emerging biomedical imaging modality that exploits the nonlinear response of superparamagnetic iron oxide (SPIO) nanoparticles to image their spatial distribution. Previously, various scanning trajectories were analyzed for the system function reconstruction (SFR) approach, providing important insight regarding their image quality performances. While Cartesian trajectories remain the most popular choice for x-space-based reconstruction, recent work suggests that non-Cartesian trajectories such as the Lissajous trajectory may prove beneficial for improving image quality. In this work, we propose a generalized reconstruction scheme for x-space MPI that can be used in conjunction with any scanning trajectory. The proposed technique automatically tunes the reconstruction parameters from the scanning trajectory, and does not induce any additional blurring. To demonstrate the proposed technique, we utilize five different trajectories with varying density levels. Comparison to alternative reconstruction methods show significant improvement in image quality achieved by the proposed technique. Among the tested trajectories, the Lissajous and bidirectional Cartesian trajectories prove more favorable for x-space MPI, and the resolution of the images from these two trajectories can further be improved via deblurring. The proposed fully automated gridding reconstruction can be utilized with these trajectories to improve the image quality in x-space MPI.

Enhanced reconstruction in magnetic particle imaging by whitening and randomized SVD approximation

Magnetic particle imaging (MPI) is a medical imaging modality of recent origin, and it exploits the nonlinear magnetization phenomenon to recover a spatially dependent concentration of nanoparticles. In practice, image reconstruction in MPI is frequently carried out by standard Tikhonov regularization with nonnegativity constraint, which is then minimized by a Kaczmarz type method. In this work, we revisit two issues in the numerical reconstruction in MPI in the lens of inverse theory, i.e. the choice of fidelity and acceleration, and propose two algorithmic tricks, i.e. a whitening procedure to incorporate the noise statistics and accelerating Kaczmarz iteration via randomized SVD. The two tricks are straightforward to implement and easy to incorporate in existing reconstruction algorithms. Their significant potentials are illustrated by extensive numerical experiments on a publicly available dataset.

Efficient Joint Image Reconstruction of Multi-Patch Data Reusing a Single System Matrix in Magnetic Particle Imaging

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.

Trajectory analysis for field free line magnetic particle imaging

PURPOSE

Magnetic particle imaging (MPI) is a relatively new method to image the spatial distribution of magnetic nanoparticle (MNP) tracers administered to the body with high spatial and temporal resolution using an inhomogeneous magnetic field. The spatial information of the MNP's is encoded using a field free point (FFP), or a field free line (FFL), in which the magnetic field vanishes at a point, or on a line, respectively. FFL scanning has the advantage of improved sensitivity compared to FFP scanning as a result of higher signal-to-noise ratio. The trajectory traversed by the FFL or FFP is an important parameter of the MPI system and should be selected to achieve the best imaging quality in minimum scan time, while considering hardware constraints and patient safety. In this study, we analyzed the image quality of different FFL trajectories for a large field of view (FOV) using simulations, to provide a baseline information for FFL scanning MPI system design.

METHODS

We simulated a human-sized FFL scanning MPI configuration to image a circular FOV with 160 mm diameter, and compared Radial, Spiral, Uniform Spiral, Flower, and Lissajous trajectories with different trajectory densities scanned by the FFL for constant scan time. We analyzed the system matrices of the trajectories in terms of mutual coherence and homogeneity of the spatial sensitivity. We calculated the maximum electric fields induced on a homogeneous conductive body by the selection field (SF) and the focus field (FF) to compare the trajectories based on the nerve stimulation threshold. The images were obtained using the system matrix reconstruction approach with two different image reconstruction methods. In the first one, we used the conventional image reconstruction method, algebraic reconstruction technique (ART), which gives a regularized least-squares solution. In the second one, we used the state-of-the-art alternating direction method of multipliers (ADMM), which minimizes a weighted sum of the l₁ -norm and the total variation (TV) of the images.

RESULTS

The Radial and Spiral trajectories resulted in a poor imaging performance at low trajectory densities due to relatively high coherency and poor sensitivity of the measurements, respectively. For ART reconstruction, the highest image quality with the lowest trajectory density was achieved with the Uniform Spiral trajectory. Uniform Spiral, Flower, and Lissajous trajectories yielded comparable performance with ADMM reconstruction. The rotating SF induced higher electric field amplitude compared to the FF. Consequently, maximum allowable gradient at the same trajectory density was greater for the Radial trajectory compared to the other trajectories.

CONCLUSIONS

For a large FOV coverage, the Uniform Spiral trajectory offers a good compromise between image quality and imaging time, taking safety and hardware limitations into account. The Radial trajectory, especially using l₁ -norm and TV priors in the reconstruction, may be favorable in case the SF induced electric field is higher than that of the FF at the same frequency (e.g., relatively small FOV coverage). In general, ADMM reconstruction resulted in higher contrast and resolution compared to ART, leading to lighter requirements on the density of the trajectory.

Bimodal intravascular volumetric imaging combining OCT and MPI

PURPOSE

Intravascular optical coherence tomography (IVOCT) is a catheter-based image modality allowing for high-resolution imaging of vessels. It is based on a fast sequential acquisition of A-scans with an axial spatial resolution in the range of 5-10 μm, that is, one order of magnitude higher than in conventional methods like intravascular ultrasound or computed tomography angiography. However, position and orientation of the catheter in patient coordinates cannot be obtained from the IVOCT measurements alone. Hence, the pose of the catheter needs to be established to correctly reconstruct the three-dimensional vessel shape. Magnetic particle imaging (MPI) is a three-dimensional tomographic, tracer-based, and radiation-free image modality providing high temporal resolution with unlimited penetration depth. Volumetric MPI images are angiographic and hence suitable to complement IVOCT as a comodality. We study simultaneous bimodal IVOCT MPI imaging with the goal of estimating the IVOCT pullback path based on the 3D MPI data.

METHODS

We present a setup to study and evaluate simultaneous IVOCT and MPI image acquisition of differently shaped vessel phantoms. First, the influence of the MPI tracer concentration on the optical properties required for IVOCT is analyzed. Second, using a concentration allowing for simultaneous imaging, IVOCT and MPI image data are acquired sequentially and simultaneously. Third, the luminal centerline is established from the MPI image volumes and used to estimate the catheter pullback trajectory for IVOCT image reconstruction. The image volumes are compared to the known shape of the phantoms.

RESULTS

We were able to identify a suitable MPI tracer concentration of 2.5 mmol/L with negligible influence on the IVOCT signal. The pullback trajectory estimated from MPI agrees well with the centerline of the phantoms. Its mean absolute error ranges from 0.27 to 0.28 mm and from 0.25 mm to 0.28 mm for sequential and simultaneous measurements, respectively. Likewise, reconstructing the shape of the vessel phantoms works well with mean absolute errors for the diameter ranging from 0.11 to 0.21 mm and from 0.06 to 0.14 mm for sequential and simultaneous measurements, respectively.

CONCLUSIONS

Magnetic particle imaging can be used in combination with IVOCT to estimate the catheter trajectory and the vessel shape with high precision and without ionizing radiation.

Moving table magnetic particle imaging: a stepwise approach preserving high spatio-temporal resolution

Magnetic particle imaging (MPI) is a highly sensitive imaging method that enables the visualization of magnetic tracer materials with a temporal resolution of more than 46 volumes per second. In MPI, the size of the field of view (FoV) scales with the strengths of the applied magnetic fields. In clinical applications, those strengths are limited by peripheral nerve stimulation, specific absorption rates, and the requirement to acquire images of high spatial resolution. Therefore, the size of the FoV is usually a few cubic centimeters. To bypass this limitation, additional focus fields and/or external object movements can be applied. The latter approach is investigated. An object is moved through the scanner bore one step at a time, whereas the MPI scanner continuously acquires data from its static FoV. Using a 3-D phantom and dynamic 3-D in vivo data, it is shown that the data from such a moving table experiment can be jointly reconstructed after reordering the data with respect to the stepwise object shifts and heart beat phases.

Magnetic particle imaging in vascular medicine

Magnetic particle imaging (MPI) is a new medical imaging technique that enables three-dimensional real-time imaging of a magnetic tracer material. Although it is not yet in clinical use, it is highly promising, especially for vascular and interventional imaging. The advantages of MPI are that no ionizing radiation is necessary, its high sensitivity enables the detection of very small amounts of the tracer material, and its high temporal resolution enables real-time imaging, which makes MPI suitable as an interventional imaging technique. As MPI is a tracer-based imaging technique, functional imaging is possible by attaching specific molecules to the tracer material. In the first part of this article, the basic principle of MPI will be explained and a short overview of the principles of the generation and spatial encoding of the tracer signal will be given. After this, the used tracer materials as well as their behavior in MPI will be introduced. A subsequent presentation of selected scanner topologies will show the current state of research and the limitations researchers are facing on the way from preclinical toward human-sized scanners. Furthermore, it will be briefly shown how to reconstruct an image from the tracer materials' signal. In the last part, a variety of possible future clinical applications will be presented with an emphasis on vascular imaging, such as the use of MPI during cardiovascular interventions by visualizing the instruments. Investigations will be discussed, which show the feasibility to quantify the degree of stenosis and diagnose strokes and traumatic brain injuries as well as cerebral or gastrointestinal bleeding with MPI. As MPI is not only suitable for vascular medicine but also offers a broad range of other possible applications, a selection of those will be briefly presented at the end of the article.

Multi-Channel Acquisition for Isotropic Resolution in Magnetic Particle Imaging

Magnetic Particle Imaging (MPI), a molecular imaging modality that images biocompatible superparamagnetic iron oxide tracers, is well-suited for clinical angiography, in vivo cell tracking, cancer detection, and inflammation imaging. MPI is sensitive and quantitative to tracer concentration, with a positive contrast that is not attenuated or corrupted by tissue background. Like other clinical imaging techniques, such as computed tomography, magnetic resonance imaging, and nuclear medicine, MPI can be modeled as a linear and shift-invariant system with a well-defined point spread function (PSF) capturing the system blur. The key difference, as we show here, is that the MPI PSF is highly dependent on scanning parameters and is anisotropic using only a single-imaging trajectory. This anisotropic resolution poses a major challenge for clear and accurate clinical diagnosis. In this paper, we generalize a tensor imaging theory for multidimensional x-space MPI to explore the physical source of this anisotropy, present a multi-channel scanning algorithm to enable isotropic resolution, and experimentally demonstrate isotropic MPI resolution through the construction and the use of two orthogonal excitation and detector coil pairs.

Calibration-Free Relaxation-Based Multi-Color Magnetic Particle Imaging

Magnetic particle imaging (MPI) is a novel imaging modality with important potential applications, such as angiography, stem cell tracking, and cancer imaging. Recently, there have been efforts to increase the functionality of MPI via multi-color imaging methods that can distinguish the responses of different nanoparticles, or nanoparticles in different environmental conditions. The proposed techniques typically rely on extensive calibrations that capture the differences in the harmonic responses of the nanoparticles. In this paper, we propose a method to directly estimate the relaxation time constant of the nanoparticles from the MPI signal, which is then used to generate a multi-color relaxation map. The technique is based on the underlying mirror symmetry of the adiabatic MPI signal when the same region is scanned back and forth. We validate the proposed method via simulations, and via experiments on our in-house magnetic particle spectrometer setup at 10.8 kHz and our in-house MPI scanner at 9.7 kHz. Our results show that nanoparticles can be successfully distinguished with the proposed technique, without any calibration or prior knowledge about the nanoparticles.

Improved sensitivity and limit-of-detection using a receive-only coil in magnetic particle imaging

Magnetic particle imaging (MPI) is an imaging modality capable of quantitatively determining the 3D distribution of a magnetic nanoparticle (MNP) ensemble. In this work, we present a method for reducing the MNP limit of detection by employing a new receive-only coil (Rx-coil) for signal acquisition. The new signal detector is designed to improve the sensitivity and thus quality of reconstructed images. We present characterization measurements conducted with the prototype Rx-coil installed in a preclinical MPI scanner. The gradiometric design of the Rx-coil attenuates the unwanted signal contributions arising from the excitation field, leading to a 17 dB lower background level compared to the conventional dual-purpose coil (TxRx-coil), which is crucial for detecting low amounts of MNP. Network analyzer measurements of the frequency-dependent coil sensitivity, as well as spectral analysis of recorded MPI data demonstrate an overall increase of the coil sensitivity of about  +12 dB for the Rx-coil. Comparisons of the sensitivity distributions revealed no significant degradations in terms of homogeneity for the Rx-coil compared to the TxRx-coil in an imaging volume of 6  ×  3  ×  3 cm³. Finally, the limit of detection was determined experimentally for each coil type using a serial dilution of MNPs, resulting in values of 133 ng of iron for the conventional TxRx-coil and 20 ng for the new Rx-coil, using an acquisition time of 2 s. A linear relationship between the reconstructed signal intensities and the iron mass in the samples was observed with coefficients of determination (R²) of above 99% in the range of the limit of detection to 3 10³ng(Fe). These results open the way for improved image quality and faster acquisition time in pre-clinical MPI scanners.

Joint Reconstruction of Tracer Distribution and Background in Magnetic Particle Imaging

Magnetic particle imaging (MPI) is a novel tomographic imaging technique, which visualizes the distribution of a magnetic nanoparticle-based tracer material. However, reconstructed MPI images often suffer from an insufficiently compensated image background caused by rapid non-deterministic changes in the background signal of the imaging device. In particular, the signal-to-background ratio (SBR) of the images is reduced for lower tracer concentrations or longer acquisitions. The state-of-the-art procedure in MPI is to frequently measure the background signal during the sample measurement. Unfortunately, this requires a removal of the entire object from the scanner's field of view (FOV), which introduces dead time and repositioning artifacts. To overcome these considerable restrictions, we propose a novel method that uses two consecutive image acquisitions as input parameters for a simultaneous reconstruction of the tracer distribution, as well as the background signal. The two acquisitions differ by just a small spatial shift, while keeping the object always within the focus of a slightly reduced FOV. A linearly interpolated background between the initial and final background measurement is used to seed the iterative reconstruction. The method has been tested with simulations and phantom measurements. Overall, a substantial reduction of the image background was observed, and the image SBR is increased by a factor of 2(7) for the measurement (simulation) data.

Synthesis, Functionalization, and Design of Magnetic Nanoparticles for Theranostic Applications

In order to translate nanotechnology into medical practice, magnetic nanoparticles (MNPs) have been presented as a class of non-invasive nanomaterials for numerous biomedical applications. In particular, MNPs have opened a door for simultaneous diagnosis and brisk treatment of diseases in the form of theranostic agents. This review highlights the recent advances in preparation and utilization of MNPs from the synthesis and functionalization steps to the final design consideration in evading the body immune system for therapeutic and diagnostic applications with addressing the most recent examples of the literature in each section. This study provides a conceptual framework of a wide range of synthetic routes classified mainly as wet chemistry, state-of-the-art microfluidic reactors, and biogenic routes, along with the most popular coating materials to stabilize resultant MNPs. Additionally, key aspects of prolonging the half-life of MNPs via overcoming the sequential biological barriers are covered through unraveling the biophysical interactions at the bio-nano interface and giving a set of criteria to efficiently modulate MNPs' physicochemical properties. Furthermore, concepts of passive and active targeting for successful cell internalization, by respectively exploiting the unique properties of cancers and novel targeting ligands are described in detail. Finally, this study extensively covers the recent developments in magnetic drug targeting and hyperthermia as therapeutic applications of MNPs. In addition, multi-modal imaging via fusion of magnetic resonance imaging, and also innovative magnetic particle imaging with other imaging techniques for early diagnosis of diseases are extensively provided.

Fast multiresolution data acquisition for magnetic particle imaging using adaptive feature detection

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.

Electronically rotated and translated field-free line generation for open bore magnetic particle imaging

PURPOSE

We propose a coil arrangement for open bore field-free line (FFL) magnetic particle imaging (MPI) system, which is suitable for accessing the subject from the sides. The purpose of this study is twofold, to show that the FFL can be rotated and translated electronically in a volume of interest with this arrangement and to analyze the current, voltage and power requirements for a 1 T/m gradient human sized scanner for a 200 mm diameter × 200 mm height cylindrical field of view (FOV).

METHODS

We used split coils side by side with alternating current directions to generate a field-free line. Employing two of these coil groups, one of which is rotated 90 degrees with respect to the other, a rotating FFL was generated. We conducted numerical simulations to show the feasibility of this arrangement for three-dimensional (3D) electronical scan of the FFL. Using simulations, we obtained images of a two-dimensional (2D) in silico dot phantom for a human size scanner with system matrix-based reconstruction.

RESULTS

Simulations showed that the FFL can be generated and rotated in one plane and can be translated in two axes, allowing for 3D imaging of a large subject with the proposed arrangement. Human sized scanner required 63-215 kW power for the selection field coils to scan the focus inside the FOV.

CONCLUSIONS

The proposed setup is suitable for FFL MPI imaging with an open bore configuration without the need for mechanical rotation, which is preferable for clinical usage in terms of imaging time and patient access. Further studies are necessary to determine the limitations imposed by peripheral nerve stimulation, and to optimize the system parameters and the sequence design.

Magnetic Particle Imaging for Real-Time Perfusion Imaging in Acute Stroke

The fast and accurate assessment of cerebral perfusion is fundamental for the diagnosis and successful treatment of stroke patients. Magnetic particle imaging (MPI) is a new radiation-free tomographic imaging method with a superior temporal resolution, compared to other conventional imaging methods. In addition, MPI scanners can be built as prehospital mobile devices, which require less complex infrastructure than computed tomography (CT) and magnetic resonance imaging (MRI). With these advantages, MPI could accelerate the stroke diagnosis and treatment, thereby improving outcomes. Our objective was to investigate the capabilities of MPI to detect perfusion deficits in a murine model of ischemic stroke. Cerebral ischemia was induced by inserting of a microfilament in the internal carotid artery in C57BL/6 mice, thereby blocking the blood flow into the medial cerebral artery. After the injection of a contrast agent (superparamagnetic iron oxide nanoparticles) specifically tailored for MPI, cerebral perfusion and vascular anatomy were assessed by the MPI scanner within seconds. To validate and compare our MPI data, we performed perfusion imaging with a small animal MRI scanner. MPI detected the perfusion deficits in the ischemic brain, which were comparable to those with MRI but in real-time. For the first time, we showed that MPI could be used as a diagnostic tool for relevant diseases in vivo, such as an ischemic stroke. Due to its shorter image acquisition times and increased temporal resolution compared to that of MRI or CT, we expect that MPI offers the potential to improve stroke imaging and treatment.

Towards Picogram Detection of Superparamagnetic Iron-Oxide Particles Using a Gradiometric Receive Coil

Superparamagnetic iron-oxide nanoparticles can be used in medical applications like vascular or targeted imaging. Magnetic particle imaging (MPI) is a promising tomographic imaging technique that allows visualizing the 3D nanoparticle distribution concentration in a non-invasive manner. The two main strengths of MPI are high temporal resolution and high sensitivity. While the first has been proven in the assessment of dynamic processes like cardiac imaging, it is unknown how far the detection limit of MPI can be lowered. Within this work, we will present a highly sensitive gradiometric receive-coil unit combined with a noise-matching network tailored for the imaging of mice. The setup is capable of detecting 5 ng of iron in-vitro with an acquisition time of 2.14 sec. In terms of iron concentration we are able to detect 156 μg/L marking the lowest value that has been reported for an MPI scanner so far. In-vivo MPI mouse images of a 512 ng bolus and a 21.5 ms acquisition time allow for capturing the flow of an intravenously injected tracer through the heart of a mouse. Since it has been rather difficult to compare detection limits across MPI publications we propose guidelines to improve the comparability of future MPI studies.

Detection and Compensation of Periodic Motion in Magnetic Particle Imaging

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-to-noise 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-to-noise ratio, blockwise 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 paper, 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 heartbeat. 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 heartbeat.

Magnetic particle imaging: from proof of principle to preclinical applications

Tomographic imaging has become a mandatory tool for the diagnosis of a majority of diseases in clinical routine. Since each method has its pros and cons, a variety of them is regularly used in clinics to satisfy all application needs. Magnetic particle imaging (MPI) is a relatively new tomographic imaging technique that images magnetic nanoparticles with a high spatiotemporal resolution in a quantitative way, and in turn is highly suited for vascular and targeted imaging. MPI was introduced in 2005 and now enters the preclinical research phase, where medical researchers get access to this new technology and exploit its potential under physiological conditions. Within this paper, we review the development of MPI since its introduction in 2005. Besides an in-depth description of the basic principles, we provide detailed discussions on imaging sequences, reconstruction algorithms, scanner instrumentation and potential medical applications.

Hybrid system calibration for multidimensional magnetic particle imaging

Magnetic particle imaging visualizes the spatial distribution of superparamagnetic nanoparticles. Because of its key features of excellent sensitivity, high temporal and spatial resolution and biocompatibility of the tracer material it can be used in multiple medical imaging applications. The common reconstruction technique for Lissajous-type trajectories uses a system matrix that has to be previously acquired in a time-consuming calibration scan, leading to long downtimes of the scanning device. In this work, the system matrix is determined by a hybrid approach. Using the hybrid system matrix for reconstruction, the calibration downtime of the scanning device can be neglected. Furthermore, the signal to noise ratio of the hybrid system matrix is much higher, since the size of the required nanoparticle sample can be chosen independently of the desired voxel size. As the signal to noise ratio influences the reconstruction process, the resulting images have better resolution and are less affected by artefacts. Additionally, a new approach is introduced to address the background signal in image reconstruction. The common technique of subtraction of the background signal is replaced by extending the system matrix with an entry that represents the background. It is shown that this approach reduces artefacts in the reconstructed images.