A simulation study on the resolution and sensitivity of magnetic particle imaging

Magnetic Particle Imaging: From Tracer Design to Biomedical Applications in Vasculature Abnormality

Magnetic particle imaging (MPI) is an emerging non-invasive tomographic technique based on the response of magnetic nanoparticles (MNPs) to oscillating drive fields at the center of a static magnetic gradient. In contrast to magnetic resonance imaging (MRI), which is driven by uniform magnetic fields and projects the anatomic information of the subjects, MPI directly tracks and quantifies MNPs in vivo without background signals. Moreover, it does not require radioactive tracers and has no limitations on imaging depth. This article first introduces the basic principles of MPI and important features of MNPs for imaging sensitivity, spatial resolution, and targeted biodistribution. The latest research aiming to optimize the performance of MPI tracers is reviewed based on their material composition, physical properties, and surface modifications. While the unique advantages of MPI have led to a series of promising biomedical applications, recent development of MPI in investigating vascular abnormalities in cardiovascular and cerebrovascular systems, and cancer are also discussed. Finally, recent progress and challenges in the clinical translation of MPI are discussed to provide possible directions for future research and development.

Solving the MPI reconstruction problem with automatically tuned regularization parameters

In the field of medical imaging, magnetic particle imaging (MPI) poses a promising non-ionizing tomographic technique with high spatial and temporal resolution. In MPI, iterative solvers are used to reconstruct the particle distribution out of the measured voltage signal based on a system matrix. The amount of regularization needed to reconstruct an image of good quality differs from measurement to measurement, depending on the MPI system and the measurement settings. Finding the right choice for the three major parameters controlling the regularization is commonly done by hand and requires time and experience. In this work, we study the reduction to a single regularization parameter and propose a method that enables automatic reconstruction. The method is qualitatively and quantitatively validated on several MPI data sets showing promising results.

A Low-Cost, Tabletop LOD-EPR System for Nondestructive Quantification of Iron Oxide Nanoparticles in Tissues

Iron oxide nanoparticles (IONPs) have wide utility in applications from drug delivery to the rewarming of cryopreserved tissues. Due to the complex behavior of IONPs (e.g., uneven particle distribution and aggregation), further developments and clinical translation can be accelerated by having access to a noninvasive method for tissue IONP quantification. Currently, there is no low-cost method to nondestructively track IONPs in tissues across a wide range of concentrations. This work describes the performance of a low-cost, tabletop, longitudinally detected electron paramagnetic resonance (LOD-EPR) system to address this issue in the field of cryopreservation, which utilizes IONPs for rewarming of rat kidneys. A low-cost LOD-EPR system is realized via simultaneous transmit and receive using MHz continuous-wave transverse excitation with kHz modulation, which is longitudinally detected at the modulation frequency to provide both geometric and frequency isolation. The accuracy of LOD-EPR for IONP quantification is compared with NMR relaxometry. Solution measurements show excellent linearity (R2 > 0.99) versus Fe concentration for both measurements on EMG308 (a commercial nanoparticle), silica-coated EMG308, and PEG-coated EMG308 in water. The LOD-EPR signal intensity and NMR longitudinal relaxation rate constant (R1) of water are affected by particle coating, solution viscosity, and particle aggregation. R1 remains linear but with a reduced slope when in cryoprotective agent (CPA) solution, whereas the LOD-EPR signal is relatively insensitive to this. R1 does not correlate well with Fe concentration in rat kidney sections (R2 = 0.3487), while LOD-EPR does (R2 = 0.8276), with a linear regression closely matching that observed in solution and CPA.

Fusion Networks of CNN and Transformer with Channel Attention for Accurate Tumor Imaging in Magnetic Particle Imaging

BACKGROUND

Magnetic Particle Imaging (MPI) is an emerging molecular imaging technique. However, since X-space reconstruction ignores system properties, it can lead to blurring of the reconstructed image, posing challenges for accurate quantification. To address this issue, we propose the use of deep learning to remove the blurry artifacts; (2) Methods: Our network architecture consists of a combination of Convolutional Neural Network (CNN) and Transformer. The CNN utilizes convolutional layers to automatically extract pixel-level local features and reduces the size of feature maps through pooling layers, effectively capturing local information within the images. The Transformer module is responsible for extracting contextual features from the images and efficiently capturing long-range dependencies, enabling a more effective modeling of global features in the images. By combining the features extracted by both CNN and Transformer, we capture both global and local features simultaneously, thereby improving the quality of reconstructed images; (3) Results: Experimental results demonstrate that the network effectively removes blurry artifacts from the images, and it exhibits high accuracy in precise tumor quantification. The proposed method shows superior performance over the state-of-the-art methods; (4) Conclusions: This bears significant implications for the image quality improvement and clinical application of MPI technology.

Progressive Pretraining Network for 3D System Matrix Calibration in Magnetic Particle Imaging

Magnetic particle imaging (MPI) is an emerging technique for determining magnetic nanoparticle distributions in biological tissues. Although system-matrix (SM)-based image reconstruction offers higher image quality than the X-space-based approach, the SM calibration measurement is time-consuming. Additionally, the SM should be recalibrated if the tracer's characteristics or the magnetic field environment change, and repeated SM measurement further increase the required labor and time. Therefore, fast SM calibration is essential for MPI. Existing calibration methods commonly treat each row of the SM as independent of the others, but the rows are inherently related through the coil channel and frequency index. As these two elements can be regarded as additional multimodal information, we leverage the transformer architecture with a self-attention mechanism to encode them. Although the transformer has shown superiority in multimodal fusion learning across several fields, its high complexity may lead to overfitting when labeled data are scarce. Compared with labeled SM (i.e., full size), low-resolution SM data can be easily obtained, and fully using such data may alleviate overfitting. Accordingly, we propose a pseudo-label-based progressive pretraining strategy to leverage unlabeled data. Our method outperforms existing calibration methods on a public real-world OpenMPI dataset and simulation dataset. Moreover, our method improves the resolution of two in-house MPI scanners without requiring full-size SM measurements. Ablation studies confirm the contributions of modeling SM inter-row relations and the proposed pretraining strategy.

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.

Anisotropic edge-preserving network for resolution enhancement in unidirectional Cartesian magnetic particle imaging

Objective. Magnetic particle imaging (MPI) is a novel imaging modality. It is crucial to acquire accurate localization of the superparamagnetic iron oxide nanoparticles distributions in MPI. However, the spatial resolution of unidirectional Cartesian trajectory MPI exhibits anisotropy, which blurs the boundaries of MPI images and makes precise localization difficult. In this paper, we propose an anisotropic edge-preserving network (AEP-net) to alleviate the anisotropic resolution of MPI.Methods. AEP-net resolve the resolution anisotropy by constructing an asymmertic convolution. To recover the edge information, we design the uncertainty region module. In addition, we evaluated the performance of the proposed AEP-net model by using simulations and experimental data.Results. The results show that the AEP-net model alleviates the anisotropy of the unidirectional Cartesian trajectory and preserves edge details in the MPI image. By comparing the visualization results and the metrics, we demonstrate that our method is superior to other methods.Significance. The proposed method produces accurate visualization in unidirectional Cartesian devices and promotes accurate quantization, which promote the biomedical applications using MPI.

Deep learning for improving the spatial resolution of magnetic particle imaging

Objective.Magnetic particle imaging (MPI) is a new medical, non-destructive, imaging method for visualizing the spatial distribution of superparamagnetic iron oxide nanoparticles. In MPI, spatial resolution is an important indicator of efficiency; traditional techniques for improving the spatial resolution may result in higher costs, lower sensitivity, or reduced contrast.Approach.Therefore, we propose a deep-learning approach to improve the spatial resolution of MPI by fusing a dual-sampling convolutional neural network (FDS-MPI). An end-to-end model is established to generate high-spatial-resolution images from low-spatial-resolution images, avoiding the aforementioned shortcomings.Main results.We evaluate the performance of the proposed FDS-MPI model through simulation and phantom experiments. The results demonstrate that the FDS-MPI model can improve the spatial resolution by a factor of two.Significance.This significant improvement in MPI could facilitate the preclinical application of medical imaging modalities in the future.

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.

Motion compensation for non-periodic dynamic tracer distributions in multi-patch magnetic particle imaging

Objective. While the spatial and temporal resolution of magnetic particle imaging is very high, the size of the field of view is limited due to physiological constraints. Multi-patch scans allow for covering larger areas by sequentially scanning smaller subvolumes, so-called patches. The visualization of tracer dynamics with a high temporal resolution are of particular interest in many applications, e.g. cardiovascular interventions or blood flow measurements. The reconstruction of non-periodic dynamic tracer distributions is currently realized by the reconstruction of a time-series of frames under the assumption of nearly static behavior during the scan of each frame. While this approach is feasible for limited velocities, it results in data gaps in multi-patch scans leading thus to artifacts for strong dynamics. In this article, we are aiming for the reconstruction of dynamic tracer concentrations with high velocities and the compensation of motion and multi-patch artifacts. Approach. We present a reconstruction method for dynamic tracer distributions using a dynamic forward model and representing the concentration within each voxel by a spline curve. The method is evaluated with simulated single- and multi-patch data. Main results. The dynamic model enables for the reconstruction of fast tracer dynamics from few frames and the spline approach approximates the missing data which reduces multi-patch artifacts. Significance. The presented method allows to compensate motion and multi-patch artifacts and to reconstruct fast dynamic tracer distributions with arbitrary motion patterns.

Magnetic particle imaging: tracer development and the biomedical applications of a radiation-free, sensitive, and quantitative imaging modality

Magnetic particle imaging (MPI) is an emerging tracer-based modality that enables real-time three-dimensional imaging of the non-linear magnetisation produced by superparamagnetic iron oxide nanoparticles (SPIONs), in the presence of an external oscillating magnetic field. As a technique, it produces highly sensitive radiation-free tomographic images with absolute quantitation. Coupled with a high contrast, as well as zero signal attenuation at-depth, there are essentially no limitations to where that can be imaged within the body. These characteristics enable various biomedical applications of clinical interest. In the opening sections of this review, the principles of image generation are introduced, along with a detailed comparison of the fundamental properties of this technique with other common imaging modalities. The main feature is a presentation on the up-to-date literature for the development of SPIONs tailored for improved imaging performance, and developments in the current and promising biomedical applications of this emerging technique, with a specific focus on theranostics, cell tracking and perfusion imaging. Finally, we will discuss recent progress in the clinical translation of MPI. As signal detection in MPI is almost entirely dependent on the properties of the SPION employed, this work emphasises the importance of tailoring the synthetic process to produce SPIONs demonstrating specific properties and how this impacts imaging in particular applications and MPI's overall performance.

Modelling of Dynamic Behaviour in Magnetic Nanoparticles

The efficient development and utilisation of magnetic nanoparticles (MNPs) for applications in enhanced biosensing relies on the use of magnetisation dynamics, which are primarily governed by the time-dependent motion of the magnetisation due to externally applied magnetic fields. An accurate description of the physics involved is complex and not yet fully understood, especially in the frequency range where Néel and Brownian relaxation processes compete. However, even though it is well known that non-zero, non-static local fields significantly influence these magnetisation dynamics, the modelling of magnetic dynamics for MNPs often uses zero-field dynamics or a static Langevin approach. In this paper, we developed an approximation to model and evaluate its performance for MNPs exposed to a magnetic field with varying amplitude and frequency. This model was initially developed to predict superparamagnetic nanoparticle behaviour in differential magnetometry applications but it can also be applied to similar techniques such as magnetic particle imaging and frequency mixing. Our model was based upon the Fokker-Planck equations for the two relaxation mechanisms. The equations were solved through numerical approximation and they were then combined, while taking into account the particle size distribution and the respective anisotropy distribution. Our model was evaluated for Synomag®-D70, Synomag®-D50 and SHP-15, which resulted in an overall good agreement between measurement and simulation.

2D Quantitative Imaging of Magnetic Nanoparticles by an AC Biosusceptometry Based Scanning Approach and Inverse Problem

The use of magnetic nanoparticles (MNPs) in biomedical applications requires the quantitative knowledge of their quantitative distribution within the body. AC Biosusceptometry (ACB) is a biomagnetic technique recently employed to detect MNPs in vivo by measuring the MNPs response when exposed to an alternate magnetic field. The ACB technique presents some interesting characteristics: non-invasiveness, low operational cost, high portability, and no need for magnetic shielding. ACB conventional methods until now provided only qualitative information about the MNPs’ mapping in small animals. We present a theoretical model and experimentally demonstrate the feasibility of ACB reconstructing 2D quantitative images of MNPs’ distributions. We employed an ACB single-channel scanning approach, measuring at 361 sensor positions, to reconstruct MNPs’ spatial distributions. For this, we established a discrete forward problem and solved the ACB system’s inverse problem. Thus, we were able to determine the positions and quantities of MNPs in a field of view of 5×5×1 cm3 with good precision and accuracy. The results show the ACB system’s capabilities to reconstruct the quantitative spatial distribution of MNPs with a spatial resolution better than 1 cm, and a sensitivity of 1.17 mg of MNPs fixed in gypsum. These results show the system’s potential for biomedical application of MNPs in several studies, for example, electrochemical-functionalized MNPs for cancer cell targeting, quantitative sensing, and possibly in vivo imaging.

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.

Engineering of magnetic nanoparticles as magnetic particle imaging tracers

Magnetic particle imaging (MPI) has recently emerged as a promising non-invasive imaging technique because of its signal linearly propotional to the tracer mass, ability to generate positive contrast, low tissue background, unlimited tissue penetration depth, and lack of ionizing radiation. The sensitivity and resolution of MPI are highly dependent on the properties of magnetic nanoparticles (MNPs), and extensive research efforts have been focused on the design and synthesis of tracers. This review examines parameters that dictate the performance of MNPs, including size, shape, composition, surface property, crystallinity, the surrounding environment, and aggregation state to provide guidance for engineering MPI tracers with better performance. Finally, we discuss applications of MPI imaging and its challenges and perspectives in clinical translation.

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.

Partial FOV Center Imaging (PCI): A Robust X-Space Image Reconstruction for Magnetic Particle Imaging

Magnetic Particle Imaging (MPI) is an emerging medical imaging modality that images the spatial distribution of superparamagnetic iron oxide (SPIO) nanoparticles using their nonlinear response to applied magnetic fields. In standard x-space approach to MPI, the image is reconstructed by gridding the speed-compensated nanoparticle signal to the instantaneous position of the field free point (FFP). However, due to safety limits on the drive field, the field-of-view (FOV) needs to be covered by multiple relatively small partial field-of-views (pFOVs). The image of the entire FOV is then pieced together from individually processed pFOVs. These processing steps can be sensitive to non-ideal signal conditions such as harmonic interference, noise, and relaxation effects. In this work, we propose a robust x-space reconstruction technique, Partial FOV Center Imaging (PCI), with substantially simplified pFOV processing. PCI first forms a raw image of the entire FOV by mapping MPI signal directly to pFOV center locations. The corresponding MPI image is then obtained by deconvolving this raw image by a compact kernel, whose fully-known shape solely depends on the pFOV size. We analyze the performance of the proposed reconstruction via extensive simulations, as well as imaging experiments on our in-house FFP MPI scanner. The results show that PCI offers a trade-off between noise robustness and interference robustness, outperforming standard x-space reconstruction in terms of both robustness against non-ideal signal conditions and image quality.

The Applications of Magnetic Particle Imaging: From Cell to Body

Magnetic particle imaging (MPI) is a cutting-edge imaging technique that is attracting increasing attention. This novel technique collects signals from superparamagnetic nanoparticles as its imaging tracer. It has characteristics such as linear quantitativity, positive contrast, unlimited penetration, no radiation, and no background signal from surrounding tissue. These characteristics enable various medical applications. In this paper, we first introduce the development and imaging principles of MPI. Then, we discuss the current major applications of MPI by dividing them into four categories: cell tracking, blood pool imaging, tumor imaging, and visualized magnetic hyperthermia. Even though research on MPI is still in its infancy, we hope this discussion will promote interest in the applications of MPI and encourage the design of tracers tailored for MPI.

Emerging methods in radiology

Imaging modalities have developed rapidly in recent decades. In addition to improved resolution as well as whole-body and faster image acquisition, the possibilities of functional and molecular examination of tissue pathophysiology have had a decisive influence on imaging diagnostics and provided ground-breaking knowledge. Many promising approaches are currently being pursued to increase the application area of devices and contrast media and to improve their sensitivity and quantitative informative value. These are complemented by new methods of data processing, multiparametric data analysis, and integrated diagnostics. The aim of this article is to provide an overview of technological innovations that will enrich clinical imaging in the future, and to highlight the resultant diagnostic options. These relate to the established imaging methods such as CT, MRI, ultrasound, PET, and SPECT but also to new methods such as magnetic particle imaging (MPI), optical imaging, and photoacoustics. In addition, approaches to radiomic image evaluation are explained and the chances and difficulties for their broad clinical introduction are discussed. The potential of imaging to describe pathophysiological relationships in ever increasing detail, both at whole-body and tissue level, can in future be used to better understand the mechanistic effect of drugs, to preselect patients to therapies, and to improve monitoring of therapy success. Consequently, the use of interdisciplinary integrated diagnostics will greatly change and enrich the profession of radiologists.

Sight and switch off: Nerve density visualization for interventions targeting nerves in prostate cancer

Nerve density is associated with prostate cancer (PCa) aggressiveness and prognosis. Thus far, no visualization methods have been developed to assess nerve density of PCa in vivo. We compounded propranolol-conjugated superparamagnetic iron oxide nerve peptide nanoparticles (PSN NPs), which achieved the nerve density visualization of PCa with high sensitivity and high specificity, and facilitated assessment of nerve density and aggressiveness of PCa using magnetic resonance imaging and magnetic particle imaging. Moreover, PSN NPs facilitated targeted therapy for PCa. PSN NPs increased the survival rate of mice with orthotopic PCa to 83.3% and decreased nerve densities and proliferation indexes by more than twofold compared with the control groups. The present study, thus, developed a technology to visualize the nerve density of PCa and facilitate targeted neural drug delivery to tumors to efficiently inhibit PCa progression. Our study provides a potential basis for clinical imaging and therapeutic interventions targeting nerves in PCa.

Multiresolution vessel detection in magnetic particle imaging using wavelets and a Gaussian mixture model

PURPOSE

Magnetic particle imaging is a tomographic imaging technique that allows one to measure the spatial distribution of superparamagnetic nanoparticles, which are used as tracer. The magnetic particle imaging scanner measures the voltage induced due to the nonlinear magnetization behavior of the nanoparticles. The tracer distribution can be reconstructed from the voltage signal by solving an inverse problem. A possible application is the imaging of vessel structures. In this and many other cases, the tracer is only located inside the structures and a large part of the image is related to background. A detection of the tracer support in early stages of the reconstruction process could improve reconstruction results.

METHODS

In this work, a multiresolution wavelet-based reconstruction combined with a segmentation of the foreground structures is performed. For this, different wavelets are compared with respect to their reconstruction quality. For the detection of the foreground, a segmentation with a Gaussian mixture model is performed, which leads to a threshold-based binary segmentation. This segmentation is done on a coarse level of the reconstruction and then transferred to the next finer level, where it is used as prior knowledge for the reconstruction. This is repeated until the finest resolution is reached.

RESULTS

The approach is evaluated on simulated vessel phantoms and on two real measurements. The results show that this method improves the structural similarity index of the reconstructed images significantly. Among the compared wavelets, the 9/7 wavelets led to the best reconstruction results.

CONCLUSIONS

The early detection of the vessel structures at low resolution helps to improve the image quality. For the wavelet decomposition, the use of 9/7 wavelets is recommended.

Quantitative Drug Release Monitoring in Tumors of Living Subjects by Magnetic Particle Imaging Nanocomposite

In vivo drug release monitoring provides accurate and reliable information to guide drug dosing. Image-based strategies for in vivo monitoring are advantageous because they are non-invasive and provide visualization of the spatial distribution of drug, but those imaging modalities in use (e.g., fluorescence imaging (FI) and magnetic resonance imaging (MRI)) remain inadequate because of the low tissue penetration depth (for FI) or difficulty with quantification of release rate and signal convolution with noise sources (for MRI). Magnetic particle imaging (MPI), employing superparamagnetic nanoparticles as the contrast agent and sole signal source, enables large tissue penetration and quantifiable signal intensity. These properties make it ideal for application to in vivo drug release monitoring. In this work, we design a superparamagnetic Fe₃O₄ nanocluster@poly(lactide-co-glycolide acid) core-shell nanocomposite loaded with a chemotherapy drug (doxorubicin) which serves as a dual drug delivery system and MPI quantification tracer. The as-prepared nanocomposite can degrade under a mild acidic microenvironment (pH = 6.5), which induces a sustained release of doxorubicin and gradual decomposition of the Fe₃O₄ nanocluster, causing the MPI signal changes. We showed that nanocomposite-induced MPI signal changes display a linear correlation with the release rate of doxorubicin over time (R² = 0.99). Utilizing this phenomenon, we successfully established quantitative monitoring of the release process in cell culture. We then performed in vivo drug release monitoring in a cancer therapy setting using a murine breast cancer model by injecting the nanocomposite, monitoring the drug release, and assessing the induced tumor cell kill. This study provides an improved solution for in vivo drug release monitoring compared to other available monitoring strategies. This translational strategy using a biocompatible polymer-coated iron oxide nanocomposite will be promising in future clinical use.

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.

Tracking the Growth of Superparamagnetic Nanoparticles with an In-Situ Magnetic Particle Spectrometer (INSPECT)

Magnetic Particle Spectroscopy (MPS) is a measurement technique to determine the magnetic properties of superparamagnetic iron oxide nanoparticles (SPIONs) in an oscillating magnetic field as applied in Magnetic Particle Imaging (MPI). State of the art MPS devices are solely capable of measuring the magnetization response of the SPIONs to an oscillatory magnetic excitation retrospectively, i.e. after the synthesis process. In this contribution, a novel in-situ magnetic particle spectrometer (INSPECT) is presented, which can be used to monitor the entire synthesis process from particle genesis via growth to the stable colloidal suspension of the nanoparticles in real time. The device is suitable for the use in a biochemistry environment. It has a chamber size of 72 mm such that a 100 ml reaction flask can be used for synthesis. For an alkaline-based precipitation, the change of magnetic properties of SPIONs during the nucleation and growth phase of the synthesis is demonstrated. The device is able to record the changes in the amplitude and phase spectra, and, in turn, the hysteresis. Hence, it is a powerful tool for an in-depth understanding of the nanoparticle formation dynamics during the synthesis process.

Multifrequency magnetic particle imaging enabled by a combined passive and active drive field feed-through compensation approach

PURPOSE

Magnetic particle imaging (MPI) allows fast imaging of the spatial distribution of superparamagnetic iron-oxide based nanoparticles (SPIONs). Recent research suggests that MPI furthermore promises in-vivo access to environmental parameters of SPIONs as temperature or viscosity. Various medical applications as nanomedicine, stem cell-based therapies or magnetic hyperthermia could benefit from in-vivo multiparameter estimation by MPI. One possible approach to get access to functional parameters is particle excitation at multiple frequencies. To enable the investigation of the mentioned approach, a novel MPI device capable of multifrequency excitation is needed.

METHODS

MPI usually employs analog band-stop filters to cancel the drive field feed-through, which is magnitudes higher than the particle signal. To enable drive field frequency flexibility over a wide bandwidth, we propose a combined passive and active drive field feed-through compensation approach. This cancellation technique further allows the direct detection of the SPIONs' signal at the fundamental excitation frequency.

RESULTS

A combined feed-through suppression of up to -125 dB is reported, which allows to adjust the drive field frequency from 500 Hz to 20 kHz. Initial spectroscopic measurements and images are shown that demonstrate the concept of multifrequency excitation and prove the imaging capability of the presented scanner. A mean signal-to-noise ratio (SNR) enhancement by the factor of 1.7 was shown when the first harmonic is used for measurement-based image reconstruction compared to when it is omitted.

CONCLUSIONS

In this paper, the first one-dimensional multifrequency magnetic particle imaging (mf-MPI) that features adjustable excitation frequencies from 500 Hz to 20 kHz is presented. The device will be used to study the principle of multiparameter estimation by employing multifrequency excitation.

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.

Clinical Tracking of Cell Transfer and Cell Transplantation: Trials and Tribulations

Cell therapy has provided unprecedented opportunities for tissue repair and cancer therapy. Imaging tools for in vivo tracking of therapeutic cells have entered the clinic to evaluate therapeutic cell delivery and retention in patients. Thus far, clinical cell tracking studies have been a mere proof of principle of the feasibility of cell detection. This review centers around the main clinical queries associated with cell therapy: Have cells been delivered correctly at the targeted site of injection? Are cells still alive, and, if so, how many? Are cells being rejected by the host, and, if so, how severe is the immune response? For stem cell therapeutics, have cells differentiated into downstream cell lineages? Is there cell proliferation including tumor formation? At present, clinical cell tracking trials have only provided information on immediate cell delivery and short-term cell retention. The next big question is if these cell tracking tools can improve the clinical management of the patients and, if so, by how much, for how many, and for whom; in addition, it must be determined whether tracking therapeutic cells in every patient is needed. To become clinically relevant, it must now be demonstrated how cell tracking techniques can inform patient treatment and affect clinical outcomes.

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.

Accomplishments and challenges in stem cell imaging in vivo

Stem cell therapies have demonstrated promising preclinical results, but very few applications have reached the clinic owing to safety and efficacy concerns. Translation would benefit greatly if stem cell survival, distribution and function could be assessed in vivo post-transplantation, particularly in patients. Advances in molecular imaging have led to extraordinary progress, with several strategies being deployed to understand the fate of stem cells in vivo using magnetic resonance, scintigraphy, PET, ultrasound and optical imaging. Here, we review the recent advances, challenges and future perspectives and opportunities in stem cell tracking and functional assessment, as well as the advantages and challenges of each imaging approach.

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.

Remote magnetic actuation using a clinical scale system

Remote magnetic manipulation is a powerful technique for controlling devices inside the human body. It enables actuation and locomotion of tethered and untethered objects without the need for a local power supply. In clinical applications, it is used for active steering of catheters in medical interventions such as cardiac ablation for arrhythmia treatment and for steering of camera pills in the gastro-intestinal tract for diagnostic video acquisition. For these applications, specialized clinical-scale field applicators have been developed, which are rather limited in terms of field strength and flexibility of field application. For a general-purpose field applicator, flexible field generation is required at high field strengths as well as high field gradients to enable the generation of both torques and forces on magnetic devices. To date, this requirement has only been met by small-scale experimental systems. We have built a highly versatile clinical-scale field applicator that enables the generation of strong magnetic fields as well as strong field gradients over a large workspace. We demonstrate the capabilities of this coil-based system by remote steering of magnetic drills through gel and tissue samples with high torques on well-defined curved trajectories. We also give initial proof that, when equipped with high frequency transmit-receive coils, the machine is capable of real-time magnetic particle imaging while retaining a clinical-scale bore size. Our findings open the door for image-guided radiation-free remote magnetic control of devices at the clinical scale, which may be useful in minimally invasive diagnostic and therapeutic medical interventions.

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.

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.

Multi-color magnetic nanoparticle imaging using magnetorelaxometry

Magnetorelaxometry (MRX) is a well-known measurement technique which allows the retrieval of magnetic nanoparticle (MNP) characteristics such as size distribution and clustering behavior. This technique also enables the non-invasive reconstruction of the spatial MNP distribution by solving an inverse problem, referred to as MRX imaging. Although MRX allows the imaging of a broad range of MNP types, little research has been done on imaging different MNP types simultaneously. Biomedical applications can benefit significantly from a measurement technique that allows the separation of the resulting measurement signal into its components originating from different MNP types. In this paper, we present a theoretical procedure and experimental validation to show the feasibility of MRX imaging in reconstructing multiple MNP types simultaneously. Because each particle type has its own characteristic MRX signal, it is possible to take this a priori information into account while solving the inverse problem. This way each particle type's signal can be separated and its spatial distribution reconstructed. By assigning a unique color code and intensity to each particle type's signal, an image can be obtained in which each spatial distribution is depicted in the resulting color and with the intensity measuring the amount of particles of that type, hence the name multi-color MNP imaging. The theoretical procedure is validated by reconstructing six phantoms, with different spatial arrangements of multiple MNP types, using MRX imaging. It is observed that MRX imaging easily allows up to four particle types to be separated simultaneously, meaning their quantitative spatial distributions can be obtained.

Magnetic Particle Imaging (MPI): Experimental Quantification of Vascular Stenosis Using Stationary Stenosis Phantoms

Magnetic Particle Imaging (MPI) is able to provide high temporal and good spatial resolution, high signal-to-noise ratio and sensitivity. Furthermore, it is a truly quantitative method as its signal strength is proportional to the concentration of its tracer, superparamagnetic iron oxide nanoparticles (SPIOs). Because of that, MPI is proposed to be a promising future method for cardiovascular imaging. Here, an interesting application may be the quantification of vascular pathologies like stenosis by utilizing the proportionality of the SPIO concentration and the MPI signal strength. In this study, the feasibility of MPI based stenosis quantification is evaluated based on this application scenario. Nine different stenosis phantoms with a normal diameter of 10 mm each and different stenoses of 1–9 mm and ten reference phantoms with a straight diameter of 1–10 mm were filled with a 1% Resovist dilution and measured in a preclinical MPI-demonstrator. The MPI signal intensities of the reference phantoms were compared to each other and the change of signal intensity within each stenosis phantom was used to calculate the degree of stenosis. These values were then compared to the known diameters of each phantom. As a second measurement, the 5 mm stenosis phantom was used for a serial dilution measurement down to a Resovist dilution of 1:3200 (0.031%), which is lower than a first pass blood concentration of a Resovist bolus in the peripheral arteries of an average adult human of at least about 1:1000. The correlation of the stenosis values based on MPI signal intensity measurements and based on the known diameters showed a very good agreement, proving the high precision of quantitative MPI in this regard.

In vivo liver visualizations with magnetic particle imaging based on the calibration measurement approach

Magnetic particle imaging (MPI) facilitates the rapid determination of 3D in vivo magnetic nanoparticle distributions. In this work, liver MPI following intravenous injections of ferucarbotran (Resovist^®) was studied. The image reconstruction was based on a calibration measurement, the so called system function. The application of an enhanced system function sample reflecting the particle mobility and aggregation status of ferucarbotran resulted in significantly improved image reconstructions. The finding was supported by characterizations of different ferucarbotran compositions with the magnetorelaxometry and magnetic particle spectroscopy technique. For instance, similar results were obtained between ferucarbotran embedded in freeze-dried mannitol sugar and liver tissue harvested after a ferucarbotran injection. In addition, the combination of multiple shifted measurement patches for a joint reconstruction of the MPI data enlarged the field of view and increased the covering of liver MPI on magnetic resonance images noticeably.

Non-Equispaced System Matrix Acquisition for Magnetic Particle Imaging Based on Lissajous Node Points

Magnetic Particle Imaging (MPI) is an emerging technology in the field of (pre)clinical imaging. The acquisition of a particle signal is realized along specific sampling trajectories covering a defined field of view (FOV). In a system matrix (SM) based reconstruction procedure, the commonly used acquisition path in MPI is a Lissajous trajectory. Such a trajectory features an inhomogeneous coverage of the FOV, i.e. the center region is sampled less dense than the regions towards the edges of the FOV. Conventionally, the respective SM acquisition and the subsequent reconstruction do not reflect this inhomogeneous coverage. Instead, they are performed on an equispaced grid. The objective of this work is to introduce a sampling grid that inherently features the aforementioned inhomogeneity by using node points of Lissajous trajectories. Paired with a tailored polynomial interpolation of the reconstructed MPI signal, the entire image can be recovered. It is the first time that such a trajectory related non-equispaced grid is used for image reconstruction on simulated and measured MPI data and it is shown that the number of sampling positions can be reduced, while the spatial resolution remains constant.

Fluorescent/magnetic micro/nano-spheres based on quantum dots and/or magnetic nanoparticles: preparation, properties, and their applications in cancer studies

The study of cancer is of great significance to human survival and development, due to the fact that cancer has become one of the greatest threats to human health. In recent years, the rapid progress of nanoscience and nanotechnology has brought new and bright opportunities to this field. In particular, the applications of quantum dots (QDs) and magnetic nanoparticles (MNPs) have greatly promoted early diagnosis and effective therapy of cancer. In this review, we focus on fluorescent/magnetic micro/nano-spheres based on QDs and/or MNPs (we may call them "nanoparticle-sphere (NP-sphere) composites") from their preparation to their bio-application in cancer research. Firstly, we outline and compare the main four kinds of methods for fabricating NP-sphere composites, including their design principles, operation processes, and characteristics (merits and limitations). The NP-sphere composites successfully inherit the unique fluorescence or magnetic properties of QDs or MNPs. Moreover, compared with the nanoparticles (NPs) alone, the NP-sphere composites show superior properties, which are also discussed in this review. Then, we summarize their recent applications in cancer research from three aspects, that is: separation and enrichment of target tumor cells or biomarkers; cancer diagnosis mainly through medical imaging or tumor biomarker detection; and cancer therapy via targeted drug delivery systems. Finally, we provide some perspectives on the future challenges and development trends of the NP-sphere composites.