First Superferromagnetic Remanence Characterization and Scan Optimization for Super-Resolution Magnetic Particle Imaging

Roadmap on magnetic nanoparticles in nanomedicine

Magnetic nanoparticles (MNPs) represent a class of small particles typically with diameters ranging from 1 to 100 nanometers. These nanoparticles are composed of magnetic materials such as iron, cobalt, nickel, or their alloys. The nanoscale size of MNPs gives them unique physicochemical (physical and chemical) properties not found in their bulk counterparts. Their versatile nature and unique magnetic behavior make them valuable in a wide range of scientific, medical, and technological fields. Over the past decade, there has been a significant surge in MNP-based applications spanning biomedical uses, environmental remediation, data storage, energy storage, and catalysis. Given their magnetic nature and small size, MNPs can be manipulated and guided using external magnetic fields. This characteristic is harnessed in biomedical applications, where these nanoparticles can be directed to specific targets in the body for imaging, drug delivery, or hyperthermia treatment. Herein, this roadmap offers an overview of the current status, challenges, and advancements in various facets of MNPs. It covers magnetic properties, synthesis, functionalization, characterization, and biomedical applications such as sample enrichment, bioassays, imaging, hyperthermia, neuromodulation, tissue engineering, and drug/gene delivery. However, as MNPs are increasingly explored forin vivoapplications, concerns have emerged regarding their cytotoxicity, cellular uptake, and degradation, prompting attention from both researchers and clinicians. This roadmap aims to provide a comprehensive perspective on the evolving landscape of MNP research.

Strongly Interacting Nanoferrites for Magnetic Particle Imaging and Spatially Resolved Thermometry

High-crystal-quality nanoferrites with short surface ligands (oleic acid) were recently shown to exhibit enhanced sensitivity and spatial resolution, likely due to chain formation (uniaxial assemblies of particles) for magnetic particle imaging (MPI). Here, we develop a simple one-pot thermal decomposition approach to produce ferrite (iron oxide) magnetic nano-objects (MNOs) that strongly interact magnetically and have good synthetic reproducibility. The ferrite MNOs were physically characterized by X-ray diffraction, Raman spectroscopy, transmission electron microscopy, and dynamic light scattering. The MNOs were magnetically characterized by magnetometry and magnetic particle spectroscopy (MPS) to study their interactions, dynamics, and suitability for spatially resolved magnetic thermometry. The MNOs were synthesized in a range of sizes between 12 nm and 27 nm, showing that MNOs below a minimum size do not exhibit dynamic interactions/significant increased response and that a larger field is required for chain formation as size increases. In addition to size effects, we explore the role of ligand length, environment (liquid vs solid), and concentration on the proposed chain formation. The experimental results were then correlated to micromagnetic simulations to gain further insight into the formation of chains. Compared to some existing MPI tracers, our ferrite MNOs exhibit enhanced signal (up to about 37×) and spatial resolution (up to about 9×) under certain limited (ferrite-MNO optimal) field and frequency conditions used. MPS as a function of temperature and drive field amplitude was performed, showing promise for spatially resolved thermometry. These results confirm the importance of tuning the frequency and amplitude of the drive field for optimal imaging/thermal performance.

RETNet: Resolution enhancement Transformer network for magnetic particle imaging based on X-space

Magnetic Particle Imaging (MPI) can visualize the concentration distribution of superparamagnetic iron-oxide nanoparticles (SPIONs) in tissues with the advantages of high sensitivity and high temporal resolution. However, the low spatial resolution of MPI limits its application. Increasing the gradient strength of the selection field can improve the resolution of MPI, but also increase power consumption and noise. A feasible and cost-effective method to address this limitation is to reconstruct high gradient (HG) image from low gradient (LG) image using algorithms. Deep learning has been a powerful tool for improving the resolution of medical imaging techniques. In this study, we propose a Resolution Enhancement Transformer Network (RETNet) for reconstructing HG image with high-resolution from LG image with low-resolution as input, avoiding high power consumption and high noise in the system with HG field. RETNet leverages a shallow feature extractor to capture shallow features, a cross-scale-Transformer (CST) to focus on textural features, a residual-swin-Transformer (RST) to focus on structural features, and an image reconstruction module to aggregate these three types of features and reconstruct the HG image. Textural and structural features extracted can ensure the integrity of the details and the realization of high definition in the reconstructed image. Ablation experiments demonstrate the significant contribution of these two modules to reconstruct the HG image. Comparative experiments, including experiments at noise-free and multiple noise levels, confirm the high robustness of RETNet. Simulation, phantom, and in vivo experiments consistently demonstrate that RETNet outperforms competing methods and effectively improves the resolution of MPI.

Magnetic nanoparticles for magnetic particle imaging (MPI): design and applications

Recent advancements in medical imaging have brought forth various techniques such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound, each contributing to improved diagnostic capabilities. Most recently, magnetic particle imaging (MPI) has become a rapidly advancing imaging modality with profound implications for medical diagnostics and therapeutics. By directly detecting the magnetization response of magnetic tracers, MPI surpasses conventional imaging modalities in sensitivity and quantifiability, particularly in stem cell tracking applications. Herein, this comprehensive review explores the fundamental principles, instrumentation, magnetic nanoparticle tracer design, and applications of MPI, offering insights into recent advancements and future directions. Novel tracer designs, such as zinc-doped iron oxide nanoparticles (Zn-IONPs), exhibit enhanced performance, broadening MPI's utility. Spatial encoding strategies, scanning trajectories, and instrumentation innovations are elucidated, illuminating the technical underpinnings of MPI's evolution. Moreover, integrating machine learning and deep learning methods enhances MPI's image processing capabilities, paving the way for more efficient segmentation, quantification, and reconstruction. The potential of superferromagnetic iron oxide nanoparticle chains (SFMIOs) as new MPI tracers further advanced the imaging quality and expanded clinical applications, underscoring the promising future of this emerging imaging modality.

Magnetic Particle Imaging Reveals that Iron-Labeled Extracellular Vesicles Accumulate in Brains of Mice with Metastases

The incidence of breast cancer remains high worldwide and is associated with a significant risk of metastasis to the brain that can be fatal; this is due, in part, to the inability of therapeutics to cross the blood-brain barrier (BBB). Extracellular vesicles (EVs) have been found to cross the BBB and further have been used to deliver drugs to tumors. EVs from different cell types appear to have different patterns of accumulation and retention as well as the efficiency of bioactive cargo delivery to recipient cells in the body. Engineering EVs as delivery tools to treat brain metastases, therefore, will require an understanding of the timing of EV accumulation and their localization relative to metastatic sites. Magnetic particle imaging (MPI) is a sensitive and quantitative imaging method that directly detects superparamagnetic iron. Here, we demonstrate MPI as a novel tool to characterize EV biodistribution in metastatic disease after labeling EVs with superparamagnetic iron oxide (SPIO) nanoparticles. Iron-labeled EVs (FeEVs) were collected from iron-labeled parental primary 4T1 tumor cells and brain-seeking 4T1BR5 cells, followed by injection into the mice with orthotopic tumors or brain metastases. MPI quantification revealed that FeEVs were retained for longer in orthotopic mammary carcinomas compared to SPIOs. MPI signal due to iron could only be detected in brains of mice bearing brain metastases after injection of FeEVs, but not SPIOs, or FeEVs when mice did not have brain metastases. These findings indicate the potential use of EVs as a therapeutic delivery tool in primary and metastatic tumors.

Open-source device for high sensitivity magnetic particle spectroscopy, relaxometry, and hysteresis loop tracing

Magnetic nanoparticles (MNPs) are used extensively across numerous disciples, with applications including Magnetic Particle Imaging (MPI), targeted hyperthermia, deep brain stimulation, immunoassays, and thermometry. The assessment of MNPs, especially those being designed for MPI, is performed with magnetic particle spectrometers, relaxometers, loop tracers, or similar devices. Despite the many applications and the need for particle assessment, there are few consolidated resources for designing or building such a MNP assessment system. Here, we describe the design and performance of an open-source device capable of spectroscopy, relaxometry, and loop tracing. We show example measurements from the device and quantify the detection sensitivity by measuring a dilution series of Synomag-D 70 nm (from 0.5 mg Fe/ml to 7 ng Fe/ml) with a 10 mT drive field at 23.8 kHz. The device measures 260 pg Fe with SNR = 1 and 1.3 ng at SNR = 5 in spectroscopy mode in under one second of measurement time. The system has a dynamic range of 60 μg to 260 pg Fe without changing the hardware configuration. As an example application, we characterize Synomag-D's relaxation time constant for drive fields 2-18 mT and compare the magnetization responses of two commonly used MNPs.

Temperature-Dependent Changes in Resolution and Coercivity of Superparamagnetic and Superferromagnetic Iron Oxide Nanoparticles

Magnetic Particle Imaging (MPI) is a tracer-based imaging modality with immense promise as a radiation-free alternative to nuclear medicine imaging techniques. Nuclear medicine requires "hot chemistry" wherein radioactive tracers must be synthesized on-site, requiring expensive infrastructure and labor costs. MPI's magnetic nanoparticles, superparamagnetic iron oxide nanoparticles (SPIOs), have no significant signal decay over time which removes cost barriers associated with nuclear medicine studies such as FDG-PET. While SPIOs are the current industry standard MPI tracer, recent developments in synthesizing superferromagnetic iron oxide nanoparticles (SFMIOs) and high resolution SPIOs (HR-SPIOs), a new class of nanoparticle with almost zero coercivity, have yielded a 30-fold improvement in resolution (0.4 mT) and SNR. To better understand the long-term performance of these new nanoparticles, this investigation reports changes in SPIO (VivoTrax Plus), HR-SPIO, and SFMIO resolution, along with SFMIO coercivity, at low temperatures (-2, 2 °C) and room temperature (18-22 °C) over 12 weeks. We find that changes in HR-SPIO resolution are more sensitive to storage temperature than SFMIOs. Additionally, we observe no appreciable difference in SFMIO coercivity between the two temperatures over time. These results can inform research on optimizing tracer synthesis while lending practical information to future hospitals about the highly accessible conditions for the transit and storage of tracers.

Magnetic Particle Imaging in Vascular Imaging, Immunotherapy, Cell Tracking, and Noninvasive Diagnosis

Magnetic particle imaging (MPI) is a new tracer-based imaging modality that is useful in diagnosing various pathophysiology related to the vascular system and for sensitive tracking of cytotherapies. MPI uses nonradioactive and easily assimilated nanometer-sized iron oxide particles as tracers. MPI images the nonlinear Langevin behavior of the iron oxide particles and has allowed for the sensitive detection of iron oxide-labeled therapeutic cells in the body. This review will provide an overview of MPI technology, the tracer, and its use in vascular imaging and cytotherapies using molecular targets.