magnetic particle imaging

Precise glioma detection is a critical challenge in the clinic. Magnetic particle imaging (MPI) is an emerging, highly sensitive medical imaging technique that has the potential to accurately detect glioma at the molecular and cellular levels. Magnetic nanoparticles (MNPs) provide an effective approach for targeted imaging to specific regions, and the morphology of MNPs plays a vital role in determining their MPI performance. MNPs with various shapes have been developed to pursue sensitive MPI, while the effect of the multivoid structure on MPI tracers is still unrevealed. Herein, we systematically investigate the impact of multivoid, yolk-shell, and completely hollow structures on the MPI signal. We identify that an increased number of magnetic cores per unit volume, decreased coercivity, and reduced full width at half-maximum of the magnetization derivative caused by the multivoid structure are the key factors that endow tracers with high MPI sensitivity. Moreover, further Arginine-Glycine-Aspartic Acid peptide modification ensures that the multivoid nanotracer exhibits high affinity and targeting to tumor cells and tissues, providing an obvious MPI signal to achieve precise glioma detection. This work enables a fundamental understanding of the effect of the multivoid structure on the MPI signal, lending guidance for designing high-performance MPI tracers for biomedical applications and promoting precise disease diagnosis.

Magnetic particle imaging (MPI) is an emerging imaging modality with exciting biomedical applications, such as cell tracking, blood pool imaging, and image-guided magnetic hyperthermia. MPI is unique in that signal is generated entirely by synthetic nanoparticle tracers, motivating precise engineering of magnetic nanoparticle properties including size, shape, composition, and coating to address the needs of specific applications. However, success in many applications and in clinical transition requires development of high-sensitivity and high-resolution tracers, for which there is considerable room for improvement. This review summarizes recent advancements in MPI tracer synthesis and compares reported tracers in terms of sensitivity and resolution. In making these comparisons, we point out inconsistencies in reporting of MPI tracer properties. To overcome this challenge, we propose a list of properties to standardize characterization and reporting of new MPI tracers and improve communication within the field.

Magnetic particle imaging (MPI) allows for radiation-free visualization of tracers without background signal. With the first human-sized interventional MPI scanner being recently developed, the aim of the present study was to test its performance for guiding of endovascular procedures in a realistic perfusion model. Three fresh-frozen cadaveric legs were prepared to establish continuous circulation in the superficial femoral artery via introducer sheaths in the inguinal and infragenicular region. To facilitate vessel visualization, a mixture of a MPI tracer (Resotran® or Perimag®) and X-ray contrast agent was injected under continuous extracorporeal perfusion and imaged simultaneously with MPI angiography and digital subtraction angiography (DSA) as reference. The MPI scanner integrates seamlessly into the standard operating procedures in the angiography suite and simultaneous imaging with DSA and MPI is feasible. The MPI scanner detects a tracer bolus of 2 ml Perimag® or 1.5 ml Resotran®. Imaging results are consistent and reproducible in three cadaveric leg phantoms. This study demonstrates, that the recently developed human-sized MPI scanner facilitates reliable radiation-free image guidance for peripheral vascular interventions in the superficial femoral artery with a tracer approved for use in humans. Doctors often use imaging techniques to enable procedures to be undertaken inside blood vessels without having to use major surgery. However, many of these methods involve radiation. Magnetic Particle Imaging (MPI) can provide clear images without using radiation. We used a MPI scanner on legs from deceased humans and showed we could visualize the femoral artery following injection with a blood vessel tracer. Our results show that MPI could be used to guide procedures instead of traditional X-ray imaging. Thus MPI could become a safer alternative to guide blood vessel interventions in the future. Hartung et al. investigate the use of magnetic particle imaging (MPI) for radiation-free guidance of vascular procedures. Using a cadaveric perfusion model, they successfully visualize the femoral artery with MPI, demonstrating MPI’s potential as a safe, reliable alternative for vascular interventions.

Theranostic platforms, combining diagnostic and therapeutic approaches within one system, have garnered interest in augmenting invasive surgical, chemical, and ionizing interventions. Magnetic particle imaging (MPI) offers a quite recent alternative to established radiation-based diagnostic modalities with its versatile tracer material (superparamagnetic iron oxide nanoparticles, SPION). It also offers a bimodal theranostic framework that can combine tomographic imaging with therapeutic techniques using the very same SPION. Methods: We show the interleaved combination of MPI-based imaging, therapy (highly localized magnetic fluid hyperthermia (MFH)) and therapy safety control (MPI-based thermometry) within one theranostic platform in all three spatial dimensions using a commercial MPI system and a custom-made heating insert. The heating characteristics as well as theranostic applications of the platform were demonstrated by various phantom experiments using commercial SPION. Results: We have shown the feasibility of an MPI-MFH-based theranostic platform by demonstrating high spatial control of the therapeutic target, adequate MPI-based thermometry, and successful in situ interleaved MPI-MFH application. Conclusions: MPI-MFH-based theranostic platforms serve as valuable tools that enable the synergistic integration of diagnostic and therapeutic approaches. The transition into in vivo studies will be essential to further validate their potential, and it holds promising prospects for future advancements.

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.

In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution achieved by using relaxometry in order to measure the magnetic fields. MPI presents 2D and 3D images with high temporal resolution, non‐ionizing radiation, and optimal visual contrast due to its lack of background tissue signal. Traditionally, the images were reconstructed by the conversion of signal from the induced voltage by generating system matrix and X‐space based methods. Because image reconstruction and analyses play an integral role in obtaining precise information from MPI signals, newer artificial intelligence‐based methods are continuously being researched and developed upon. In this work, we summarize and review the significance and employment of machine learning and deep learning models for applications with MPI and the potential they hold for the future.

Magnetic particle imaging (MPI) is an emerging modality that can address longstanding technological challenges encountered with magnetic particle hyperthermia (MPH) cancer therapy. MPI is a tracer technology compatible with MPH for which magnetic nanoparticles (MNPs) provide signal for MPI and heat for MPH. Identifying whether a specific MNP formulation is suitable for both modalities is essential for clinical implementation. Current models predict that functional requirements of each modality impose conflicting demands on nanoparticle magnetic properties. This objective here is to develop a measurement and ranking scheme based on end‐use performance to streamline evaluation of candidate MNP formulations. The measured MPI point‐spread function (PSF) and specific loss power (SLP) is combined to generate a single numerical value for comparison on a relative ranking scale, or figure of merit (FoM). 12 aqueous iron‐containing formulations are evaluated, including FDA‐approved (parenteral) iron‐containing colloids. MNPs with high (Synomag‐D70: 123.4), medium (Synomag‐D50: 63.2), and low (NanoXact: 0.147) FoM values are selected for in vivo validation of the selection scheme in subcutaneous 4T1 tumors. Results demonstrate that the proposed ranking accurately assessed the relative performance of MNPs for MPI and MPH. Data demonstrated that image quality and tumor temperature rise increased with FoM ranking, validating predictions. It isshown that the MPI signal correlated with MNP concentration in tissue. Computational heat transfer models anchored on tumor MPI data harmonized with experimental results to within an average of 2 °C when MNP content estimated from MPI data is included. Computational studies emphasized the importance of post‐injection MNP quantitation and MPI spatial resolution.

This study explored the effectiveness of nasal administration in delivering magnetic nanoparticles into the brain for magnetic particle imaging of target regions. Successful delivery of iron oxide nanoparticles, which serve as contrast agents, to specific sites within the brain is crucial for achieving magnetic particle imaging. Nasal administration has gained attention as a method to bypass the blood-brain barrier and directly deliver therapeutics to the brain. In this study, we investigated surface modification techniques for administering magnetic nanoparticles into the nasal cavity, and provided experimental validation through in vivo studies. By compositing magnetic nanoparticles with gold nanoparticles, we enabled additional surface modification via AuS bonds without compromising their magnetic properties. The migration of the designed PEGylated magnetic nanoparticles into the brain following nasal administration was confirmed by magnetization measurements. Furthermore, we demonstrated the accumulation of these nanoparticles at specific target sites using probe molecules immobilized on the PEG terminus. Thus, the efficacy of delivering magnetic nanoparticles to the brain via nasal administration was demonstrated in this study. The findings of this research are expected to contribute significantly to the realization of magnetic particle imaging of target regions within the brain.

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Minimally invasive endovascular interventions have become an important tool for the treatment of cardiovascular diseases such as ischemic heart disease, peripheral artery disease, and stroke. X-ray fluoroscopy and digital subtraction angiography are used to precisely guide these procedures, but they are associated with radiation exposure for patients and clinical staff. Magnetic Particle Imaging (MPI) is an emerging imaging technology using time-varying magnetic fields combined with magnetic nanoparticle tracers for fast and highly sensitive imaging. In recent years, basic experiments have shown that MPI has great potential for cardiovascular applications. However, commercially available MPI scanners were too large and expensive and had a small field of view (FOV) designed for rodents, which limited further translational research. The first human-sized MPI scanner designed specifically for brain imaging showed promising results but had limitations in gradient strength, acquisition time and portability. Here, we present a portable interventional MPI (iMPI) system dedicated for real-time endovascular interventions free of ionizing radiation. It uses a novel field generator approach with a very large FOV and an application-oriented open design enabling hybrid approaches with conventional X-ray-based angiography. The feasibility of a real-time iMPI-guided percutaneous transluminal angioplasty (PTA) is shown in a realistic dynamic human-sized leg model.

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.

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.

Advances in instrumentation and tracer materials are still required to enable sensitive, accurate, and localized in situ 3D temperature monitoring by magnetic particle imaging (MPI). We have developed a high-resolution magnetic particle imaging instrument and implemented a low-noise multi-harmonic lock-in detection method to observe and quantify temperature variations in iron oxide nanoparticle tracers using the harmonic ratio method for determining temperature. Using isolated harmonics for MPI and temperature imaging revealed an apparent dependence of imaging resolution on harmonic number. Thus, we present experimental and simulation studies to quantify the imaging resolution dependence on temperature and harmonic number, and directly validate the fundamental origin of MPI imaging resolution on harmonic number based on the concept of a harmonic point-spread-function.

Magnetic nanomaterials that respond to clinical magnetic devices have significant potential as cancer nanotheranostics. The complexities of their physics, however introduces challenges for these applications. Hyperthermia is a heat-based cancer therapy that improves treatment outcomes and patient survival when controlled energy delivery is combined with accurate thermometry. To date, few technologies have achieved the needed evolution for the demands of the clinic. Magnetic fluid hyperthermia (MFH) offers this potential, but to be successful it requires particle-imaging technology that provides real-time thermometry. Presently, the only technology having the potential to meet these requirements is magnetic particle imaging (MPI), for which a proof-of-principle demonstration with MFH has been achieved. Successful clinical translation and adoption of integrated MPI/MFH technology will depend on successful resolution of the technological challenges discussed.

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.

Inflammation is a pivotal driver of atherosclerotic plaque progression and rupture and is a target for identifying vulnerable plaques. However, challenges arise with the current in vivo imaging modalities for differentiating vulnerable atherosclerotic plaques from stable plaques due to their low specificity and sensitivity. Herein, we aimed to develop a novel multimodal imaging platform that specifically targets and identifies high-risk plaques in vivo by detecting active myeloperoxidase (MPO), a potential inflammatory marker of vulnerable atherosclerotic plaque. Methods: A novel multimodal imaging agent, 5-HT-Fe3O4-Cy7 nanoparticles (5HFeC NPs), used for active MPO targeting, was designed by conjugating superparamagnetic iron oxide nanoparticles (SPIONs) with 5-hydroxytryptamine and cyanine 7 N-hydroxysuccinimide ester. The specificity and sensitivity of 5HFeC NPs were evaluated using magnetic particle imaging (MPI), fluorescence imaging (FLI), and computed tomographic angiography (CTA) in an ApoE-/- atherosclerosis mouse model. Treatment with 4-ABAH, an MPO inhibitor, was used to assess the monitoring ability of 5HFeC NPs. Results: 5HFeC NPs can sensitively differentiate and accurately localize vulnerable atherosclerotic plaques in ApoE-/- mice via MPI/FLI/CTA. High MPI and FLI signals were observed in atherosclerotic plaques within the abdominal aorta, which were histologically confirmed by multiple high-risk features of macrophage infiltration, neovascularization, and microcalcification. Inhibition of active MPO reduced accumulation of 5HFeC NPs in the abdominal aorta. Accumulation of 5HFeC NPs in plaques enabled quantitative evaluation of the severity of inflammation and monitoring of MPO activity. Conclusions: This multimodal MPI approach revealed that active MPO-targeted nanoparticles might serve as a method for detecting vulnerable atherosclerotic plaques and monitoring MPO activity.

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.

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.

Magnetic particle imaging (MPI) is a novel emerging noninvasive and radiation-free imaging modality that can quantify superparamagnetic iron oxide nanoparticles tracers. The zero endogenous tissue background signal and short image scanning times ensure high spatial and temporal resolution of MPI. In the context of precision medicine, the advantages of MPI provide a new strategy for the integration of the diagnosis and treatment of diseases. In this review, after a brief explanation of the simplified theory and imaging system, we focus on recent advances in the biomedical application of MPI, including vascular structure and perfusion imaging, cancer imaging, the MPI guidance of magnetic fluid hyperthermia, the visual monitoring of cell and drug treatments, and intraoperative navigation. We finally optimize MPI in terms of the system and tracers, and present future potential biomedical applications of MPI.

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The low magnetic saturation of iron oxide nanoparticles, which are developed primarily as contrast agents for magnetic resonance imaging, limits the sensitivity of their detection using magnetic particle imaging (MPI). Here, we show that FeCo nanoparticles that have a core diameter of 10 nm and bear a graphitic carbon shell decorated with poly(ethylene glycol) provide an MPI signal intensity that is sixfold and fifteenfold higher than the signals from the superparamagnetic iron oxide tracers VivoTrax and Feraheme, respectively, at the same molar concentration of iron. We also show that the nanoparticles have photothermal and magnetothermal properties and can therefore be used for tumour ablation in mice, and that they have high optical absorbance in a broad near-infrared region spectral range (wavelength, 700–1,200 nm), making them suitable as tracers for photoacoustic imaging. As sensitive multifunctional and multimodal imaging tracers, carbon-coated FeCo nanoparticles may confer advantages in cancer imaging and hyperthermia therapy. FeCo nanoparticles with a graphitic carbon shell decorated with poly(ethylene glycol) have photothermal and magnetothermal properties and are sensitive tracers for magnetic particle imaging, magnetic resonance imaging and photoacoustic imaging.

Magnetic nanoparticles have many advantages in medicine such as their use in non‐invasive imaging as a Magnetic Particle Imaging (MPI) tracer or Magnetic Resonance Imaging contrast agent, the ability to be externally shifted or actuated and externally excited to generate heat or release drugs for therapy. Existing nanoparticles have a gentle sigmoidal magnetization response that limits resolution and sensitivity. Here it is shown that superferromagnetic iron oxide nanoparticle chains (SFMIOs) achieve an ideal step‐like magnetization response to improve both image resolution & SNR by more than tenfold over conventional MPI. The underlying mechanism relies on dynamic magnetization with square‐like hysteresis loops in response to 20 kHz, 15 kAm−1 MPI excitation, with nanoparticles assembling into a chain under an applied magnetic field. Experimental data shows a “1D avalanche” dipole reversal of every nanoparticle in the chain when the applied field overcomes the dynamic coercive threshold of dipole‐dipole fields from adjacent nanoparticles in the chain. Intense inductive signal is produced from this event resulting in a sharp signal peak. Novel MPI imaging strategies are demonstrated to harness this behavior towards order‐of‐magnitude medical image improvements. SFMIOs can provide a breakthrough in noninvasive imaging of cancer, pulmonary embolism, gastrointestinal bleeds, stroke, and inflammation imaging.

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.

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Abstract Magnetic Particle Imaging (MPI) is a new imaging modality that quantifies spatial distribution of superparamagnetic iron oxide nanoparticles in a field of view through their non-linear magnetization response to an applied magnetic field. Liposomes loaded with magnetic nanoparticles (magnetoliposomes) are potential theranostic drug carriers that can combine magnetic properties with drug carrier ability. Prior work suggests that MPI signal can be affected by dipolar interactions between magnetic nanoparticles in close proximity – for instance, in a polymer matrix. Here we evaluate if encapsulating magnetic nanoparticles in liposomes has an impact in their MPI performance. For this purpose, magnetoliposomes were prepared using charge-stabilized iron oxide nanoparticles (IONPs) and their physical, magnetic and MPI properties were characterized. Interestingly, encapsulation of IONPs in magnetoliposomes led to an increase in MPI signal per unit mass.

Aim: Magnetic particle imaging (MPI) is highly promising for biomedical applications, but optimal tracers for MPI, namely superparamagnetic iron oxide-based contrast agents, are still lacking. Materials & methods: The encapsulation of commercially available nanoparticles, specifically synomag®-D and perimag®, into human red blood cells (RBCs) was performed by a hypotonic dialysis and isotonic resealing procedure. The amounts of superparamagnetic iron oxide incorporated into RBCs were determined by Fe quantification using nuclear magnetic resonance and magnetic particle spectroscopy. Results: Perimag-COOH nanoparticles were identified as the best nanomaterial for encapsulation in RBCs. Perimag-COOH-loaded RBCs proved to be viable cells showing a good magnetic particle spectroscopy performance, while the magnetic signal of synomag-D-COOH-loaded RBCs dropped sharply. Conclusion: Perimag-COOH-loaded RBCs could be a potential tool for MPI diagnostic applications.

Magnetic particle imaging (MPI) is a promising medical imaging technique for visualizing the three-dimensional distribution of tracer materials, specifically iron oxide nanoparticles (IONP). The optimization of magnetic nanoparticles (MNP) plays an essential role to improve the image resolution and sensitivity of imaging techniques. Objective. In this work, the optimization of commercial IONP (EMG 700, Ferrotec) coated with anionic surfactants was carried out using magnetic separation (MS) technique, by a low gradient magnetic separation (LGMS) (<15 T m−1) method, to improve their performance as MPI tracers. Approach. The magnetophoretical behavior of the samples in different concentrations ranging from 2 to 120 mmol l−1 was investigated over 24 h of separation. The samples were characterized by dynamic light scattering (DLS), AC susceptibility (ACS), magnetic particle spectroscopy (MPS) and they were imaged in a preclinical MPI scanner, before and after MS. Main results. DLS results showed that by increasing the concentration from 2 to 120 mmol l−1 the hydrodynamic diameter of MNP decrease from 75 to 47 nm and size distribution decrease from 0.19 to 0.11 after 4 min MS. In addition, the MPS results demonstrated the third harmonic amplitude normalized to the iron amount (A3*) and harmonic ratio A5/A3 of signal increase from 8.38 to 10.59 Am2 kg−1 (Fe) and 24.21–26.60, respectively. Furthermore, the MPI images of the samples after separation showed higher MPI resolution. Significance. Therefore, LGMS can be considered as a valuable method to narrow and control the size distribution of MNP for MPI.

Nanoparticle tracers with small sizes and large magnetization are critical for biomedical imaging and especially for magnetic particle imaging (MPI). Small size is important for accessing future intracellular and neurological in vivo applications Here, we show <15 nm nanoparticles made of zero valent iron cores, iron oxide shells and coated with a strongly binding brush co-polymer are effective MPI tracers. The small nanoparticle cores create a hydrodynamic diameter that is half of the state-of-the-art iron oxide tracers while the strongly magnetic zero valent iron maintains similar MPI signal magnitude and resolution.

Superparamagnetic iron oxide nanoparticles (SPIONs) are a foundational platform for a variety of biomedical applications. Of particular interest is Magnetic Particle Imaging (MPI), which is a growing area of research and development due to its advantages including high resolution and sensitivity with positive contrast. There has been significant work in the area of in vivo optimization of SPIONs for MPI as well as their biodistribution in and clearance from the body. However, little is known about the dynamics of SPIONs following cellular internalization which may limit their usefulness in a variety of potential imaging and treatment applications. This work shows a clear 20% decrease in magnetic performance of SPIONs, as observed by Magnetic Particle Spectroscopy (MPS), after internalization and systematic consideration of applicable factors that affect SPION signal generation, including microstructure, environment, and interparticle interactions. There is no observed change to SPION microstructure after internalization, and the surrounding environment plays little to no role in magnetic response for the SPIONs studied here. Interparticle interactions described by dipole-dipole coupling of SPIONs held close to one another after internalization are shown to be the dominant cause of decreased magnetic performance in cells. These conclusions were drawn from transmission electron microscopy (TEM) image analysis at relevant length scales, experimentally prepared and characterized SPIONs in varied environmental conditions, and theoretical modeling with Monte Carlo simulations.

Superparamagnetic iron oxide (SPIO) nanoparticles with optimized and well-characterized properties are critical for Magnetic Particle Imaging (MPI). MPI is a novel in vivo imaging modality that promises to integrate the speed of CT, safety of MRI and sensitivity of PET. Since SPIOs are the source of MPI signal, both the core and surface properties must be optimized to enable efficient in vivo imaging with pharmacokinetics tailored for specific imaging applications. Existing SPIOs like Resovist (ferucarbotran) provide suboptimal MPI signal, and further limit MPI's in vivo utility due to rapid systemic clearance. An SPIO agent with a long blood half-life (t1/2) would be a versatile MPI tracer with widespread applications. Here we show a long circulating polyethylene glycol (PEG)-coated SPIO tracer, LS-008, provides excellent colloidal stability and persistent intravascular MPI signal, showing potential as the first blood pool tracer optimized for MPI. We evaluated variations of the PEG coating and found that colloidal stability of tracers improved with increasing PEG molecular weight (keeping PEG loading constant). Blood circulation in mice, evaluated using Magnetic Particle Spectrometry (MPS), showed that t1/2 of SPIO tracers varied with both PEG molecular weight and loading. LS-008, coated with 20 kDa PEG at 18.8% loading capacity, provided the most promising long-term colloidal stability with a t1/2 of about 105 minutes in mice. In vivo MPI imaging with LS-008 in a 7 T/m/μ0 3D x-space MPI mouse scanner revealed a prolonged intravascular signal (3-5 hours) post-injection. Our results show the optimized magnetic properties a nd long systemic retention of LS-008 make it a promising blood pool MPI tracer, with potential to enable MPI imaging in cardio - and cerebrovascular disease models, and solid tumor quantification and imaging via enhanced permeation and retention.

Magnetic Particle Imaging (MPI) is an emerging, whole body biomedical imaging technique, with sub-millimeter spatial resolution and high sensitivity to a biocompatible contrast agent consisting of an iron oxide nanoparticle core and a biofunctionalized shell. Successful application of MPI to imaging of cancer depends on the nanoparticles (NPs) accumulating at tumors at sufficient levels relative to other sites. NPs physiochemical properties such as size, crystallographic structure and uniformity, surface coating, stability, blood circulation time and magnetization determine the efficacy of their tumor accumulation and MPI signal generation. Here, we address these criteria by presenting strategies for the synthesis and surface functionalization of efficient MPI tracers, that can target a typical murine brain cancer model and generate three dimensional images of these tumors with very high signal-to-noise ratios (SNR). Our results showed high contrast agent sensitivities that enabled us to detect 1.1ng of iron (SNR~3.9) and enhance the spatial resolution to about 600μm. The biodistribution of these NPs was also studied using near infra-red fluorescent (NIRF) and single-photon emission computed tomography (SPECT) imaging. NPs were mainly accumulated in liver and spleen and did not show any renal clearance. This first pre-clinical study of cancer targeted NPs imaged using a tomographic MPI system in an animal model, paves the way to explore new nanomedicine strategies for cancer diagnosis and therapy, using clinically safe magnetic iron oxide nanoparticles and MPI.

Multi-functional nanoparticles with a magnetic core and gold shell structures are emerging multi-modal imaging probes for disease diagnosis, image-guided therapy, and theranostic applications. Owing to their multi-functional magnetic and plasmonic properties, these nanoparticles can be used as contrast agents in multiple complementary imaging modalities. Magnetic particle imaging (MPI) is a new pre-clinical imaging system that enables real-time imaging with high sensitivity and spatial resolution by detecting the dynamic responses of nanoparticle tracers. In this study, we evaluated dynamic magnetic properties and MPI imaging performances of core-shell nanoparticles with magnetic core coated with gold shell. A change in AC hysteresis loops was detected before and after the formation of gold shell on magnetic core nanoparticles, suggesting the influence of core-shell interfacial effect on their dynamic magnetic properties. This alteration in the dynamic responses resulted in an enhancement of MPI imaging capacity of magnetic nanoparticles. The gold shell coating also enabled a simple and effective functionalization of the nanoparticles with a brain glioma targeting ligand. The enhanced MPI imaging capacity and effective functionality suggest the potential application of the magnetic-gold core-shell nanoparticles for MPI disease diagnostics.

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Objective: Frequency selection is a crucial step for calibration-based magnetic particle imaging (MPI) reconstruction, enabling improvement in computational efficiency and noise suppression. Current methods combine signal-to-noise ratio (SNR) feature with a selection threshold. However, the selection threshold determination is experience-dependent, and the utilization of the system matrix (SM) and the imaging phantom signal is insufficient. Method: To suppress these issues, an adaptive and robust frequency selection framework (AR-FSF) is proposed, including three modules: (i) Velocity-corrected feature calculation, which limits feature calculation to the calibration points with high field-free-region velocity, (ii) Adaptive threshold calculation, which adaptively calculates the noise level using the feature spectrum, (iii) Forward-backward selection, which selects high-SNR frequency components for both SM and imaging phantom for reconstruction. Results: Signal experiments validate the effectiveness and the robustness of the introduced modules respectively. Reconstruction experiments further validate that the AR-FSF method can provide a simple and robust frequency selection process for reconstruction. In experiments using in-house data, the AR-FSF method provides suitable frequency components for fast and high-quality imaging, requiring a minimum reconstruction time of 4.5% compare to current methods. Conclusion: The proposed AR-FSF method effectively simplifies the frequency selection process, enabling adaptive selection of frequency component for different phantoms, thereby achieving fast and high-quality reconstruction. Significance: The AR-FSF method simplifies the frequency component selection process and can be widely applied in calibration-based MPI reconstruction, laying a methodological foundation for future biomedical applications.

BACKGROUND Magnetic particle imaging (MPI) is a recently developed, non-invasive in vivo imaging technique to map the spatial distribution of superparamagnetic iron oxide nanoparticles (SPIONs) in animal tissues with high sensitivity and speed. It is a challenge to reconstruct images directly from the received signals of MPI device due to the complex physical behavior of the nanoparticles. System matrix and X-space are two commonly used MPI reconstruction methods, where the former is extremely time-consuming and the latter usually produces blurry images. PURPOSE Currently, we proposed an end-to-end machine learning framework to reconstruct high-resolution MPI images from 1-D voltage signals directly and efficiently. METHODS The proposed framework, which we termed "MPIGAN", was trained on a large MPI simulation dataset containing 291 597 pairs of high-resolution 2-D phantom images and each image's corresponding voltage signals, so that it was able to accurately capture the nonlinear relationship between the spatial distribution of SPIONs and the received voltage signal, and realized high-resolution MPI image reconstruction. RESULTS Experiment results showed that, MPIGAN exhibited remarkable abilities in high-resolution MPI image reconstruction. MPIGAN outperformed the traditional methods of system matrix and X-space in recovering the fine-scale structure of magnetic nanoparticles' spatial distribution and achieving enhanced reconstruction performance in both visual effects and quantitative assessments. Moreover, even when the received signals were severely contaminated with noise, MPIGAN could still generate high-quality MPI images. CONCLUSION Our study provides a promising AI solution for end-to-end, efficient, and high-resolution magnetic particle imaging reconstruction.

Magnetic particle imaging is a novel tomographic imaging technique that enables noninvasive and highly sensitive imaging of superparamagnetic iron oxide nanoparticles distributed in living subjects. Several studies have reported on the development of reconstruction algorithms; however, a unified software framework for magnetic particle imaging reconstruction has yet to be developed. Herein, we propose a high‐performance, flexible, and easy‐to‐use magnetic particle imaging reconstruction framework using the Python programming language. The magnetic particle imaging reconstruction framework consists of the data access, preprocessing, image reconstruction, and postprocessing phases. We used the proposed framework to simulate the x‐space and system matrix‐based reconstruction methods with Cartesian and Lissajous scan trajectories. The reconstruction results of an open magnetic particle imaging dataset and a numerically simulated phantom demonstrated that the magnetic particle imaging reconstruction framework provides a reliable and accessible environment for magnetic particle imaging reconstruction, which can be extended and customized.

Objective. Magnetic particle imaging (MPI) is a promising imaging modality that leverages the nonlinear magnetization behavior of superparamagnetic iron oxide nanoparticles to determine their concentration distribution. Previous optimization models with multiple regularization terms have been proposed to achieve high-quality MPI reconstruction, but these models often result in increased computational burden, particularly for dense gridding 3D fields of view. In order to achieve faster reconstruction speeds without compromising reconstruction quality, we have developed a novel fused LASSO operator, total sum-difference (TSD), which effectively captures the sparse and smooth priors of MPI images. Methods. Through an analysis-synthesis equivalence strategy and a constraint smoothing strategy, the TSD regularized model was solved using the fast iterative soft-thresholding algorithm (FISTA). The resulting reconstruction method, TSD-FISTA, boasts low computational complexity and quadratic convergence rate over iterations. Results. Experimental results demonstrated that TSD-FISTA required only 10% and 37% of the time to achieve comparable or superior reconstruction quality compared to commonly used fused LASSO-based alternating direction method of multipliers and Tikhonov-based algebraic reconstruction techniques, respectively. Significance. TSD-FISTA shows promise for enabling real-time 3D MPI reconstruction at high frame rates for large fields of view.

Magnetic Particle Imaging (MPI) is a promising biomedical imaging modality renowned for its high sensitivity. However, MPI reconstruction poses a fundamental yet severely ill-posed inverse problem, where accurate quantitative reconstruction is essential for its practical applications. Recently, diffusion models have shown great potential in addressing such inverse problems due to their generative flexibility and robustness. Nevertheless, existing diffusion-based methods often overlook structural information from auxiliary modalities, which can provide valuable anatomical priors to enhance reconstruction fidelity. In this work, we propose DiMAGnet, a multimodal diffusion-based framework for MPI reconstruction. To ensure consistency with physical measurements, we incorporate a range-null space decomposition that integrates the system matrix and measured signal directly into the diffusion process. Additionally, the proposed Structure-guided Temporal Enhancement Processing (STEP) module adaptively fuses anatomical information from external modalities across diffusion steps. To overcome the inefficiency of iterative sampling and remove dependence on structural images during inference, we further develop a knowledge distillation strategy, resulting in a lightweight student model (LiteMAGnet) capable of single-step, structure-free reconstruction. Extensive experiments on both simulated and public OpenMPI datasets demonstrate that DiMAGnet outperforms state-of-the-art methods in terms of image quality and concentration accuracy. Moreover, LiteMAGnet provides a practical and efficient solution for scenarios lacking structural data, achieving nearly 50× faster reconstruction compared to DiMAGnet. The code will be available at DiMAGnet.

Magnetic particle imaging (MPI) is an emerging tomographic technique that utilizes the nonlinear magnetization response of superparamagnetic iron oxide nanoparticles to provide high-contrast and high-spatial-resolution images. However, image quality can be affected by various noise sources, including harmonic interference, Gaussian noise, and ambient noise, which can reduce the signal-to-noise ratio and introduce artifacts. To address these challenges, we propose a novel end-to-end neural network approach for MPI reconstruction, which can learn complex nonlinear relationships in training data, including complex background noise features and missing higher order harmonic information. Unlike traditional methods, the neural network we propose can directly reconstruct images from raw signals, effectively simplifying the image reconstruction process. In addition, we have also made improvements in the hardware part, which can effectively improve the signal-to-noise ratio of the signals. The experimental results show that our method effectively enhances the quality of the reconstructed image, and significantly accelerates the reconstruction process.

No abstract available

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.

High-quality imaging in low-concentration scenarios is a key issue for promoting the future clinical application of MPI. To address this, the Low Concentration Reconstruction Challenge in Magnetic Particle Imaging (LCR-MPI) has been organized in association with ISBI in 2026. In this paper, we present Concentration Level Enhancement And Recovery in MPI reconstruction (CLEAR-MPI), integrating advantages of the sinogram-domain denosing and image-domain denosing. The dual-domain module uses the Swin Image Restoration (SwinIR) to extract features from sinogram domain and image domain. The sinogram-domain enhancement improves the stability of the conversion process, while image-domain refinement corrects residual artifacts and restores structural details. The total score over the test dataset, including the indistribution (IID) and out-of-distribution (OOD) noise conditions, has improved significantly. Our solution is the second rank approach in this challenge.

Magnetic particle imaging (MPI) is an emerging noninvasive molecular imaging technique. In its system matrix (SM)-based MPI reconstruction problem, an ill-posed inverse problem persists—measurement noise and SM errors stemming from hardware defects or model simplifications readily induce image artifacts. To address this challenge, we propose an interpretable variational network that unfolds the gradient descent solver into a learnable iterative architecture: Within an energy minimization framework, this network balances data fidelity (ensuring consistency with measured MPI signals) and field of expertise (FoE) regularization (constraining the solution space). Each iteration is parameterized by multiscale convolutional filters and activated via radial basis functions to adaptively enhance edges and fine textures. Extensive experiments on simulated models and in vivo datasets demonstrate that this approach significantly improves reconstruction accuracy and noise resilience while faithfully preserving structural details. This validates the effectiveness of interpretable variational networks in high-precision MPI, paving the way for real-time clinically viable imaging.

Magnetic particle imaging (MPI) quantitatively visualizes the spatial distribution of magnetic nanoparticles (MNPs) by reconstructing volumetric image of their nonlinear response signals. Deep learning techniques supported by large amounts of training data have been used in MPI reconstruction. However, the acquisition of MPI data requires a long time of scanning and preprocessing. This makes it impractical and unavailable to acquire a sufficient amount of data to construct a high-quality training dataset. In this work, we propose a physical model-constrained network (PMCNet) for MPI reconstruction in which the physical model constraints are incorporated into deep learning networks to overcome the limitation of dependence on training data. The PMCNet evolves through the constraints of the physical model rather than being trained with paired data. In the method, the neural networks are run to generate a spatial distribution of MNPs, and then a physical model is used to calculate the magnetic response signals generated from this spatial distribution. The parameters of the neural networks are optimized by minimizing the error between the measured and calculated magnetic response signals. The performance of the PMCNet is evaluated with a series of simulations. The results collectively demonstrate that the PMCNet has high accuracy, high robustness, and good generalizability, facilitating high-resolution and multicolor MPI. Although PMCNet demonstrates superior performance in controlled simulations, its evaluation on the OpenMPI public dataset reveals a performance gap compared to results from synthetic data. This discrepancy primarily stems from the inherent challenge of simplified forward model in fully capturing the complex physical nonidealities present in real-world hardware environments.

Magnetic Particle Imaging (MPI) is a novel imaging technique that detects the magnetic response of superparamagnetic iron oxide nanoparticles, offering high sensitivity and temporal resolution. However, the system matrix (SM) calibration, which is crucial for quantitative analysis in MPI, remains complex, time-consuming, and prone to artifacts. To address these challenges, we propose a Distance Map Consistent System Matrix (DMC-SM) generation method. By utilizing a one-dimensional distance-domain function, DMC-SM effectively calibrates the system matrix, eliminating distortions and interpolation discontinuities. Specifically, experiments conducted with actual measurement data in closedbore MPI demonstrate that the proposed method enables a larger Field of View (FOV). Additionally, it reduces computational complexity, significantly accelerates calibration speed, and enhances image reconstruction quality. For a $64 \times 64$ grid, the system matrix construction time was reduced from 112.64s to just 2.18s, indicating substantial improvements in both speed and efficiency.

Magnetic Particle Imaging (MPI) is an emerging, radiationfree modality for quantitatively mapping superparamagnetic tracer concentration, yet liver uptake often overwhelms nearby tumor signals. We propose a physics-constrained diffusion reconstruction that injects the system matrix into a pre-trained diffusion prior, alternating denoising with a onestep data-consistency update and null-space conditioning. Without changing hardware or tracers, the method enforces physical fidelity while preserving fine structures. On simulated and measured liver-tumor phantoms, it reduces artifacts and more reliably recovers small lesions adjacent to strong liver signals compared with traditional reconstructions.

Reconstruction of low-concentration magnetic particle imaging (MPI) is a critical challenge for clinical translation, due to low signal-to-noise ratio (SNR), severe artifacts, and blurred details in the reconstructed harmonic images. The ISBI-2026 LCR-MPI Challenge is focused on achieving high-fidelity fifth-harmonic reconstruction from low-concentration data. The network takes normalized third- and fifth-harmonic images as dual-channel input and integrates a spatially adaptive denormalization-based fusion module to leverage the structural prior of the third harmonic for guiding the restoration of noisy fifth-harmonic images. To further improve the signal-to-noise ratio, a spatial-frequency dual-domain fusion scheme based on a symmetric encoder-decoder architecture with fast Fourier convolution is adopted. Additionally, a learnable gated skip connection is incorporated to adaptively regulate network information flow and enhance feature representation capability. Experiments on the challenge dataset demonstrate that DuLCR-Net outperforms the baselines, providing an effective solution for low-concentration MPI reconstruction and advancing its clinical applicability.

Magnetic particle imaging (MPI) is an innovative imaging modality offering high spatio-temporal resolution for reconstructing magnetic particle distributions. To achieve high-quality MPI images, traditional methods such as the X-space method and system matrix (SM) method operate in the time and frequency domains, respectively. Both time and frequency domain signals essentially consist of harmonic components, meaning that the quality of the reconstruction is closely tied to the accuracy of these harmonic elements. However, the presence of background noise and the complexities associated with SM collection significantly hinder harmonic quality, leading to degradation of reconstruction fidelity. To tackle these challenges, we propose a unified framework based on harmonic knowledge to enhance the quality of reconstruction. In particular, our approach involves pretraining autoencoders by restoring masked SMs, thereby modeling the relationship between harmonics. Subsequently, using appropriate decoders, the pretrained encoder can be transferred to tasks such as spectrum denoising and SM super-resolution. Our framework's effectiveness is validated on these two tasks through simulation and publicly available datasets, where it consistently outperforms state-of-the-art (SOTA) methods. In addition, our framework shows marked improvements in reconstruction quality for both time and frequency domain data collected with our in-house MPI system.

No abstract available

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.

The authors have requested that this preprint be withdrawn due to author disagreement.

Magnetic Particle Imaging (MPI) is an emerging biomedical imaging technique. The x-space method, one of the mainstream reconstruction methods in MPI, offers high efficiency and real-time capabilities but is limited by theoretical spatial resolution constraints and typically necessitates high gradient magnetic fields. This study introduces a semi-analytical reconstruction (Semi-AR) method for x-space MPI scanner, incorporating a kernel optimization step to achieve a spatial resolution better than the theoretical limit. By modeling the x-space MPI system with focus-field sequences as a linear shift invariant system, the point spread function (PSF) is decomposed into basis functions and variants across different spatial frequencies. These functions are weighted to reconstruct a high-resolution PSF, with optimal weights adaptively determined via quadratic programming. A mouse-sized MPI scanner with 3D focus-field sequences was developed to evaluate the method. Simulation and experimental results showcase Semi-AR’s superior spatial resolution and robustness compared to existing x-space techniques, particularly in detecting low-brightness targets near highlighted non-target organs. Both phantom and in vivo experiments robustly validate Semi-AR’s effectiveness, providing new insights into MPI scanner development, and advancing preclinical and potential clinical MPI applications.

Abstract Magnetic Particle Imaging (MPI) is an emerging biomedical imaging modality that detects superparamagnetic iron oxide (SPIO) nanoparticle tracers, providing high contrast, sensitivity, and quantification capabilities without the use of ionizing radiation, making it particularly suitable for cancer diagnostics. Considerable engineering efforts are underway to translate MPI technology to clinical settings. Most of these MPI scanners feature a cylindrical bore geometry similar to other clinical imaging modalities, which restricts their potential application primarily to head scanning. We have developed a single-sided MPI scanner designed to expand the technique's applicability to other regions of the human body. In this study, we demonstrate the imaging capabilities of our single-sided MPI scanner by imaging an anatomical breast phantom with concealed SPIO point sources to evaluate its potential for breast tumor diagnostics. Our method successfully distinguished these artificial tumors in two orthogonal slices, showcasing the scanner's capability to facilitate the transition of MPI technology from small animals to clinical use.

We present the design and characterization of a human-sized open-sided magnetic particle imaging scanner. The scanner is designed with a 300 mm height aperture and a cylindrical field of view (FOV) measuring 200 mm in diameter and 20 mm in height. To achieve 3D scanning, we combine a 2D electrical scan from focus coils with a mechanical scan facilitated by the discretized rotational motion of the scanner. Simulation results indicate that the designed scanner can produce high-quality magnetic fields using a reasonable power configuration, allowing for 3D imaging in just a few seconds of scan time.

Magnetic particle imaging (MPI) devices are currently advancing toward human-scale applications. We propose a novel electrically scanned field-free line (FFL) MPI architecture suitable for human-scale imaging. This design utilizes three annular gradient coil pairs to generate an electrically controllable FFL gradient field for the spatial encoding of magnetic particles. The scanner is designed with a 200 mm bore diameter and a gradient strength of 1.6 T/m. By controlling the currents in the gradient coil pairs, the FFL can be rotated and translated within the radial plane. Axial scanning is achieved via adjacent gradient coils along the axial direction. Simulations demonstrate that the designed scanner achieves human-scale electrically-driven scanning with a power consumption of 63.6 kW and can perform three-dimensional imaging within seconds.

Objective: Magnetic Particle Imaging (MPI) is a biomedical imaging modality that shows promise in enhancing clinical imaging capabilities. While there are current efforts to expand MPI hardware to enable whole-body and head imaging, this comes at a significant cost in terms of complexity and expense. Methods: A single-sided scanner can offer greater accessibility to the scanning area, as all hardware is located on one side of the device's surface, however, at the price of the limited penetration depth. Despite this, a single-sided device could serve as an open geometry preclinical scanner and a clinical instrument for local screening and procedures. The original single-sided device was introduced for a field-free point encoding gradient, which may limit its practical characteristics. Results: We developed a field-free line single-sided device that has improved imaging characteristics, which is beneficial for preclinical applications and potential clinical translation. Conclusion: We demonstrated full tomographic imaging capabilities with the prototype of a single-sided MPI scanner and characterized the imaging performance of our imaging system. Significance: MPI has the potential to image various human body regions, particularly for breast cancer diagnosis. Our research tackles the crucial aspect of scalability in the emerging MPI technique.

No abstract available

Magnetic particle imaging (MPI) is an emerging imaging modality that has a potential of complimenting other imaging modalities in clinical practice. Despite many efforts to scale up MPI hardware to date, no MPI systems have been demonstrated to accommodate full-body imaging. Previously, we introduced hardware and characterized a prototype of a single-sided MPI scanner, where all coils are confined to a single side of the device, which provides a subject with unrestricted access to the scanning area although with a limited penetration depth. The major difference in our design from the first reported single-sided scanner is in incorporating a field-free line (FFL) instead of a field-free point (FFP), which generally promises higher sensitivity and more robust image reconstruction. However, as inherent to any single-sided configurations the fields in our device are spatially inhomogeneous making it challenging to apply existing imaging techniques. For our specific geometry, we implemented spatial encoding scheme and imaging in time domain making the image reconstruction fast. In this work, we present 1-D imaging of multiple rods phantoms with a single-sided FFL MPI scanner. The results demonstrate that our scanner is capable of 1-D imaging of phantoms with a spatial resolution of at least 7 mm without image processing.

No abstract available

Magnetic particle imaging (MPI) is a new medical imaging technique visualizing the concentration distribution of superparamagnetic nanoparticles used as tracer material. MPI is not yet in clinical routine, since one of the challenges is the upscaling of scanners. Typically, the magnetic fields of MPI scanners are generated electromagnetically, resulting in an immense power consumption but providing high flexibility in terms of adjusting the field strengths and very fast image acquisition rates. Permanent magnets provide high flux densities and do not need any power supply. However, the flux density is not adjustable, and a mechanical movement is slow compared to electromagnetically varying fields. The MPI scanner concept proposed here uses permanent magnets and provides high flexibility, with the possibility to choose between fast overview scanning and detailed image acquisition. By mechanical rotation of magnetic rings in Halbach array configuration, it is possible to adjust the field or gradient strengths. The latter allows for determining the spatial resolution and the size of the field of view. A continuous mechanical rotation defines the coarseness of the scanning trajectory and image acquisition rate. This concept provides a comparable flexibility, as an alternating magnetic field and an adjustable field gradient can be applied as known from electromagnetically driven MPI systems, and therefore yields high potential for an enlarged system. We present the idea of an arrangement of Halbach arrays and how to calculate the generated magnetic fields. Simulations for an exemplary geometry are provided to show the potential of the proposed setup.

Magnetic Particle Imaging is an imaging modality that exploits the nonlinear response of superparamagnetic iron oxide nanoparticles to a time-varying magnetic field. In the past years, various scanner topologies have been proposed, which includes a single-sided scanner. Such a scanner features all its hardware located on one side, offering accessibility without limitations due to the size of the object of interest. In this paper, we present a proof of concept image reconstruction simulation studies for a single-sided field-free line scanner utilizing non-uniform magnetic fields. Specifically, we implemented a filtered backprojection algorithm allowing a 2D image reconstruction over a field of view of 4 × 4 cm2 with a spatial resolution of up to 2 mm for noiseless case.

No abstract available

This article presents the design and implementation of the magnetic particle imaging (MPI) scanner for phantom imaging of the potential tracer agents for MPI applications. The field-free point (FFP)-based spatial encoding of 4.3 T/m was implemented so that it helps to localize the superparamagnetic iron oxide nanoparticles (SPIONs) response inside the field of view (FOV). Solenoid drive coil and gradiometric receive coil were constructed to achieve 15 mT (peak amplitude) at 9.3 kHz. Moreover, a magnetic particle spectrometer (MPS) was utilized at 9.9 kHz for the characterization of relaxation time, frequency spectrum, and full-width at half-maximum (FWHM) of the particle signals in response to the excitation field at 9.9 kHz. The X-space algorithm was designed for the post-processing of the particle signals in the MPI scanner. Dextran-coated Perimag and carboxydextran-coated Vivotrax plus were characterized with MPS followed by the phantom imaging with MPI scanner. Perimag was found as fast relaxation sample (low relaxation time), narrow pulsewidth (high spatial resolution), and higher signal intensity compared to the Vivotrax plus.

No abstract available

We describe and demonstrate an approach to magnetic particle imaging in which particle excitation and field free point (FFP) manipulation are decoupled from one another. The additional degrees of freedom enabled by this decoupling suggest alternative strategies for studying and exploiting contrast mechanisms, optimizing image quality and resolution, and device-size scaling. The prototype instrument we describe uses rotating arrays of permanent magnets to scan the FFP through the field of view and current-driven oscillating magnetic fields to elicit non-linear magnetization responses from superparamagnetic nanoparticles. Narrow-band phase sensitive detection of these responses at one or more harmonics of the excitation field provides a rich source of information from which images can be reconstructed. Images generated from data acquired using this instrument are presented, demonstrating the resolution of features with sub-millimetre dimensions.We describe and demonstrate an approach to magnetic particle imaging in which particle excitation and field free point (FFP) manipulation are decoupled from one another. The additional degrees of freedom enabled by this decoupling suggest alternative strategies for studying and exploiting contrast mechanisms, optimizing image quality and resolution, and device-size scaling. The prototype instrument we describe uses rotating arrays of permanent magnets to scan the FFP through the field of view and current-driven oscillating magnetic fields to elicit non-linear magnetization responses from superparamagnetic nanoparticles. Narrow-band phase sensitive detection of these responses at one or more harmonics of the excitation field provides a rich source of information from which images can be reconstructed. Images generated from data acquired using this instrument are presented, demonstrating the resolution of features with sub-millimetre dimensions.

No abstract available

Commercial magnetic particle imaging (MPI) tracers are utilized for many applications including as reference materials for comparing tracer performance, but comparative measurements of many tracers on a single imaging platform are uncommon. This study uses relaxometry and 2D imaging in a pre-clinical MOMENTUM scanner to characterize and evaluate the performance of six common commercial tracers for MPI: Synomag-D Plain, Synomag-D NH2, Synomag-D PEG, Perimag, Vivotrax, and Vivotrax Plus. Repeated measurements of samples showed considerable variability while inter-sample differences were generally negligible. Sensitivity between the two modes is strongly correlated while resolution of the two modes was only moderately correlated. Further, relaxometric measurements tend to predict better resolution than is achieved in 2D imaging. All evaluated tracers have resolutions within 1 mm of each other on the MOMENTUM imager. These results illustrate that relaxometer measurements can be used to broadly predict MPI performance but do not accurately recreate the physical processes or differences in hardware and software of MPI. This study provides MPI measurements of tracers that may be useful to researchers while highlighting the challenges of making comparisons between tracers due to variation within samples and across different measurement techniques and instruments.

Magnetic Particle Imaging (MPI) has evolved over the past two decades from a conceptual imaging innovation to a promising modality ready for translation into clinical settings. MPI visualizes the distribution of superparamagnetic iron oxide nanoparticles (SPIONs) using time-varying magnetic fields and offers unique advantages such as high sensitivity, real-time 3D imaging, and the absence of ionizing radiation. This review traces key milestones in MPI's development across instrumentation, algorithmics, tracer systems, and medical applications. Advances in scanner technology include field-free point and field-free line architectures, with preclinical and now human-scale systems enabling imaging of extremities and the brain. Reconstruction methods in MPI have evolved along two complementary paths: system matrix-based reconstruction and x-space algorithms. Both approaches are well-established and offer distinct advantages in terms of image quality, computational efficiency, and application-specific flexibility. In parallel, nanoparticle design has advanced significantly, with novel tracers like SPION-loaded erythrocytes, magnetosomes, and SMART rhesins improving circulation time, specificity, or signal quality. MPI's sensitivity allows for diverse biomedical applications, including cardiovascular imaging, endovascular intervention guidance, stroke detection, perfusion monitoring, and cell tracking. The integration of MPI with other modalities such as MRI and CT further expands its diagnostic potential by adding an anatomical background. The recent reintroduction of a clinically approved tracer (Resotran) and the emergence of human-scale scanners mark important steps toward clinical implementation. As the field moves forward, the validation of clinical benefit through prospective trials remains a key challenge. Nevertheless, current momentum strongly suggests that MPI is ready to become a valuable tool in clinical diagnostics within the near future.

Magnetic Particle Imaging (mpi) is an emerging imaging modality with exceptional promise for clinical applications in rapid angiography, cell therapy tracking, cancer imaging, and inflammation imaging. Recent publications have demonstrated quantitative mpi across rat sized fields of view with x-space reconstruction methods. Critical to any medical imaging technology is the reliability and accuracy of image reconstruction. Because the average value of the mpi signal is lost during direct-feedthrough signal filtering, mpi reconstruction algorithms must recover this zero-frequency value. Prior x-space mpi recovery techniques were limited to 1d approaches which could introduce artifacts when reconstructing a 3d image. In this paper, we formulate x-space reconstruction as a 3d convex optimization problem and apply robust a priori knowledge of image smoothness and non-negativity to reduce non-physical banding and haze artifacts. We conclude with a discussion of the powerful extensibility of the presented formulation for future applications.

Magnetic Particle Imaging (MPI), a non-invasive imaging technique, leverages the detection of superparamagnetic nanoparticles to visualize biological processes with high sensitivity and temporal-spatial resolution. However, low tracer concentrations can diminish the signal-to-noise ratio of the magnetization signal, resulting in significant noise artifacts in MPI images reconstructed using the X-space method. Hardware improvements have high complexity, while traditional methods lack robustness to different noise levels, making it difficult to improve the quality of low concentration MPI images. In this paper, we introduced a synergistic spatial-frequency feature-guided approach for MPI image denoising and quality enhancement, based on a sparse lightweight transformer model. Specifically, the Fast Fourier Adjustment (FFA) module employs the Fast Fourier Transform and a series of convolutional operations to generate frequency-domain feature maps, which co-guide the network reconstruction with spatial-domain features. The Spatial and Frequency Guidance (SAG) module integrates pixel-level and implicit frequency detail features by learning dual-domain spatial-frequency representations. It consists of a Layer Normalization (LN), a Spatial and Frequency Fusion Attention (SFA) block, and a Multilayer Perceptron (MLP) module. Simulation experiments demonstrate that our method outperforms existing deep learning techniques in MPI image denoising and generates high-quality MPI images even at low concentrations.

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.

Objective: Magnetic particle imaging (MPI) is a promising medical modality that can image superparamagnetic iron-oxide nanoparticle (SPIO) concentration distributions safely and with high sensitivity. In the x-space reconstruction algorithm, the Langevin function is inaccurate in modeling the dynamic magnetization of SPIOs. This problem prevents the x-space algorithm from achieving a high spatial resolution reconstruction. Methods: We propose a more accurate model to describe the dynamic magnetization of SPIOs, named the modified Jiles–Atherton (MJA) model, and apply it to the x-space algorithm to improve the image resolution. Considering the relaxation effect of SPIOs, the MJA model generates the magnetization curve via an ordinary differential equation. Three modifications are also introduced to further improve its accuracy and robustness. Results: In magnetic particle spectrometry experiments, the MJA model shows higher accuracy than the Langevin and Debye models under various test conditions. The average root-mean-square error is 0.055, 83% and 58% lower than the Langevin and Debye models, respectively. In MPI reconstruction experiments, the MJA x-space improves the spatial resolution by 64% and 48% compared to the x-space and Debye x-space methods, respectively. Conclusion: The MJA model shows high accuracy and robustness in modeling the dynamic magnetization behavior of SPIOs. By integrating the MJA model into the x-space algorithm, the spatial resolution of MPI technology was improved. Significance: Improving the spatial resolution by using the MJA model helps MPI have a better performance in medical fields, including cardiovascular imaging.

Magnetic particle imaging (MPI) is an emerging tracer-based imaging modality with high sensitivity and excellent temporal resolution; however, X-space reconstruction suffers from inherent point spread function (PSF) blur that limits spatial resolution. This study aims to improve X-space MPI reconstruction quality while preserving physical consistency. To address this problem, we propose X-Space-PC-Restore, a physics-consistent deep learning framework that combines a hybrid U-Net encoder-decoder architecture with Transformer-based attention and PSF-guided loss functions. The method was evaluated on a synthetic dataset of 1600 samples spanning 4 phantom types (line, circle, ellipse, cross), Lissajous trajectories, and signal-to-noise ratio (SNR) levels from 5 to 15 dB. The proposed model achieved a peak SNR (PSNR) of 15.48 dB and an normalized root mean square error (NRMSE) of 0.175, representing a 23.0% improvement in PSNR and 45.8% reduction in NRMSE compared to the best classical baseline (Richardson–Lucy: PSNR 12.58 dB, NRMSE 0.244). The method demonstrated consistent superiority across all tested SNR levels (5–40 dB), with PSNR gains ranging from 1.4 to 3.0 dB over Richardson–Lucy. Resolution analysis showed that Richardson–Lucy achieved the best full width at half maximum (10.47 ± 0.93 pixels), while the proposed method achieved near-ground-truth resolution. SNR robustness analysis confirmed stable performance of the proposed method across noise conditions, whereas classical methods exhibited greater sensitivity to noise. These results demonstrate that physics-informed deep learning is a promising strategy for reducing blur, improving fidelity, and enhancing the reliability of MPI image reconstruction.

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.

Magnetic particle imaging (MPI) is a promising new medical imaging tracer modality with potential applications in human angiography, cancer imaging, in vivo cell tracking, and inflammation imaging. Here we demonstrate both theoretically and experimentally that multidimensional MPI is a linear shift-invariant imaging system with an analytic point spread function. We also introduce a fast image reconstruction method that obtains the intrinsic MPI image with high signal-to-noise ratio via a simple gridding operation in x-space. We also demonstrate a method to reconstruct large field-of-view (FOV) images using partial FOV scanning, despite the loss of first harmonic image information due to direct feedthrough contamination. We conclude with the first experimental test of multidimensional x-space MPI.

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.

No abstract available

Projection magnetic particle imaging (MPI) can improve imaging speed by over 100-fold over traditional 3-D MPI. In this work, we derive the 2-D x-space signal equation, 2-D image equation, and introduce the concept of signal fading and resolution loss for a projection MPI imager. We then describe the design and construction of an x-space projection MPI scanner with a field gradient of 2.35 T/m across a 10 cm magnet free bore. The system has an expected resolution of 3.5 × 8.0 mm using Resovist tracer, and an experimental resolution of 3.8 × 8.4 mm resolution. The system images 2.5 cm × 5.0 cm partial field-of views (FOVs) at 10 frames/s, and acquires a full field-of-view of 10 cm × 5.0 cm in 4 s. We conclude by imaging a resolution phantom, a complex “Cal” phantom, mice injected with Resovist tracer, and experimentally confirm the theoretically predicted x-space spatial resolution.

Magnetic particle imaging (MPI) is an emerging tomographic imaging technology that detects magnetic nanoparticle tracers by exploiting their non-linear magnetization properties. In order to predict the behavior of nanoparticles in an imager, it is possible to use a non-imaging MPI relaxometer or spectrometer to characterize the behavior of nanoparticles in a controlled setting. In this paper we explore the use of ferrohydrodynamic magnetization equations for predicting the response of particles in an MPI relaxometer. These include a magnetization equation developed by Shliomis (Sh) which has a constant relaxation time and a magnetization equation which uses a field-dependent relaxation time developed by Martsenyuk, Raikher and Shliomis (MRSh). We compare the predictions from these models with measurements and with the predictions based on the Langevin function that assumes instantaneous magnetization response of the nanoparticles. The results show good qualitative and quantitative agreement between the ferrohydrodynamic models and the measurements without the use of fitting parameters and provide further evidence of the potential of ferrohydrodynamic modeling in MPI.

Magnetic particle imaging (MPI) is a new imaging modality that noninvasively images the spatial distribution of superparamagnetic iron oxide nanoparticles (SPIOs). MPI has demonstrated high contrast and zero attenuation with depth, and MPI promises superior safety compared to current angiography methods, X-ray, computed tomography, and magnetic resonance imaging angiography. Nanoparticle relaxation can delay the SPIO magnetization, and in this work we investigate the open problem of the role relaxation plays in MPI scanning and its effect on the image. We begin by amending the X-space theory of MPI to include nanoparticle relaxation effects. We then validate the amended theory with experiments from a Berkeley x-space relaxometer and a Berkeley x-space projection MPI scanner. Our theory and experimental data indicate that relaxation reduces SNR and asymmetrically blurs the image in the scanning direction. While relaxation effects can have deleterious effects on the MPI scan, we show theoretically and experimentally that x-space reconstruction remains robust in the presence of relaxation. Furthermore, the role of relaxation in x-space theory provides guidance as we develop methods to minimize relaxation-induced blurring. This will be an important future area of research for the MPI community.

No abstract available

Objective: Magnetic Particle Imaging (MPI) is a tracer based biomedical imaging modality that enables quantitative visualization of magnetic nanoparticles (MNPs). Current MPI technology mainly focuses on single-channel imaging. In recent years, the multi-color MPI has emerged, allowing for the simultaneous imaging of multiple distinct tracers, significantly broadening MPI's application spectrum. For instance, multi-color MPI can concurrently visualize distinct cell types or molecular markers, facilitating the investigation of spatio-temporal interactions between cells or biomolecules. However, existing multi-color MPI techniques use different superparamagnetic MNPs for imaging. Their similar magnetization responses limit the imaging effect when there is a large particle signal difference. Methods: In this study, we propose a semi-periodic x-space method to use superparamagnetic and superferromagnetic particles for multi-color MPI. The method takes advantage of their distinct coercivity characteristics, allowing for robust multi-color imaging without requiring iterative solving or any additional prior information beyond coercivity. Results: We validate the feasibility and robustness of the proposed multi-color method under conditions of low signal-to-noise ratio (5 dB) and high signal intensity ratios (16:1) through simulation and in vitro experiments. Furthermore, we showcase the in vivo imaging capability using a mouse tumor model to simultaneously visualize superparamagnetic and superferromagnetic MNPs within the tumor. Conclusion: We propose a method that can effectively and robustly reconstruct superparamagnetic and superferromagnetic MNPs simultaneously in MPI. Its performance has been rigorously validated through comprehensive simulations and experiments. Significance: The proposed method successfully leverages the coercivity characteristics of superparamagnetic and superferromagnetic MNPs, improving the performance of multi-color MPI.

Magnetic particle imaging (MPI) is a tracer-based imaging modality known for its high sensitivity and temporal resolution. It makes the quantitative visualization of the concentration and spatial location of magnetic nanoparticles (MNPs) possible. The <inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>-space method is widely used in MPI for high-speed image reconstruction. In this method, the received signal is mapped directly onto the corresponding spatial position. However, the anisotropic 2-D point spread function (PSF) in the <inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>-space method introduces artifacts and blur in the reconstructed images. In this study, an isotropic MPI method, transverse MNP signal-based isotropic imaging (TSI), was developed. It makes isotropic imaging possible in 2-D unidirectional Cartesian MPI. Unlike conventional methods that rely on MNP signals registered from the same direction as the excitation, TSI only utilizes MNP signals received perpendicular to the excitation direction. In TSI, the velocity of the time domain signal is first normalized to obtain a “raw-image.” The raw-image is then integrated, differentiated, and added to generate an isotropic MPI image. Because all the operations in TSI are analytical, it preserves the real-time imaging characteristics of the <inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>-space while isotropic imaging is performed. The PSF of TSI was calculated through analytical derivation and the resolution isotropy was validated through simulation and imaging experiments on an in-house-built MPI system. The simulation and experimental results demonstrate that, by improving the isotropy, TSI achieves an approximately 2.5-<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> increase in resolution in the nonexcitation direction compared with the conventional <inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>-space method. The proposed TSI algorithm does not increase hardware complexity and requires only half the scanning time of the previously proposed multichannel method. In addition, the transverse MNP signal used in TSI is geometrically decoupled from the excitation magnetic field, eliminating the need for a special compensation design for the receiving coils. This shows promise for future MPI designs with flexible surface coils.

Motivation The sensitivity and resolution of magnetic particle imaging (MPI) depend on the choice of tracer and specific imaging parameters. For cell tracking applications with MPI, both the superparamagnetic iron oxide (SPIO) tracer and the cell labeling efficiency have a significant impact on MPI sensitivity and vary for different tracers. Objective This study compared three commercially available SPIO tracers (VivoTrax, Synomag-D and ProMag) and SPIO-labeled cells using magnetic particle relaxometry (MPR) and imaging. Further, the effect of imaging parameters (gradient field strength and drive field amplitude) on MPI signal strength, resolution, and cell detection limits, was evaluated. Results The peak MPI signal measured by MPR was much higher for Synomag-D compared to VivoTrax and ProMag. However, the signal for intracellular Synomag-D was significantly reduced. In contrast, the signal for ProMag, a micron-sized iron oxide (MPIO) particle, was not significantly different for free and intracellular particles. The cellular iron loading was higher for ProMag compared to Synomag-D. The total MPI signal measured from images of free and intracellular SPIOs was highest for ProMag. Varying imaging parameters confirmed that a lower gradient field strength and higher drive field amplitude improved tracer and cellular sensitivity. Conclusion These results show that the evaluation of tracers by MPR is not sufficient to predict the performance of all SPIO tracers; in particular, not for larger, polymer-encapsulated iron particles such as ProMag, or for SPIO tracers internalized in cells. Improvements in MPI sensitivity through lower gradient field strength and higher drive field amplitudes are associated with a trade-off in image resolution.

In vivo tracking of stem cells is critical to stem cell transplantation and regenerative medicine research. Achieving long-term, non-invasive imaging of transplanted cells demands highly sensitive imaging technology. Magnetic particle imaging (MPI) is an emerging modality capable of visualizing the distribution of superparamagnetic iron oxide nanoparticles (SPIOs). Previous studies have reported that MPI can detect clusters of approximately 2500 SPIO-labeled stem cells. However, more than this sensitivity level is needed to monitor the survival, migration, differentiation, and regeneration of stem cells. To address this limitation, we developed an ultrahigh-sensitivity magnetic particle imaging (US-MPI) device capable of detecting stem cells at scales of hundreds of cells. Through full-chain design and optimization, US-MPI maximizes the capture of SPIO signals from stem cells. Using a commercial SPIO (VivoTrax), mouse mesenchymal stem cells were labeled, and samples with varying cell numbers (ranging from 50 to 1×107) were tested using US-MPI and 9.4 T MRI (uMR 9.4T, United Imaging Life Science Instrument, Wuhan, China) for comparison. US-MPI demonstrated the ability to detect as few as 50 labeled stem cells in vitro and 150 cells in vivo, whereas 9.4 T MRI required at least 1×105 cells for detection in vitro. Thus, US-MPI exhibited a sensitivity 2, 000 times higher than that of 9.4 T MRI. The efficacy of US-MPI for stem cell tracking was further validated in a lipopolysaccharide (LPS)-induced mouse hind limb inflammation model. Eight hours after SPIO-labeled stem cells were injected intravenously, US-MPI detected strong signals on the inflamed limb, which remained significantly higher than those of the healthy limb during a 48-hour monitoring period. Post-mortem Prussian blue staining of inflamed tissues confirmed the presence of blue-stained SPIO particles, verifying that the US-MPI signals originated from labeled stem cells. These results highlight the ultrahigh sensitivity of US-MPI for stem cell tracking, providing a valuable tool for advancing stem cell research. Yimeng Li, Yu An, Yang Du, Lin Shen, Jie Tian. Development of ultrahigh sensitivity magnetic particle imaging for stem cell tracking [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2526.

Stem cell therapies are increasingly recognized as the future direction of regenerative medicine, but the biological fate of the administrated stem cells remains a major concern for clinical translation, which calls for an approach to efficiently monitoring the stem cell behaviors in vivo. Magnetic particle imaging (MPI) is an emerging technology for cell tracking, however, its utility has been largely restricted due to the lack of optimal magnetic nanoparticle tracers. Herein, by controlled engineering of the size and shape of magnetic nanoparticles tailored to MPI physic theory, a specialized MPI tracer, based on cubic iron oxide nanoparticle with an edge length of 22 nm (CIONs-22), is developed. Due to the inherent lower portion of disordered surface spins, CIONs-22 exhibit significantly larger saturation magnetization than that of spherical ones, while possess similar saturation magnetization but smaller coercivity compared to larger-sized CIONs. These magnetic properties of CIONs-22 warrant high sensitivity and resolution of MPI. With their efficient cellular uptake, CIONs-22 exhibit superior MPI performance for stem cell labeling and tracking compared to the commercialized tracer Vivotrax. By virtue of these advantages, CIONs-22 enable real-time and prolonged monitoring of the spatio-temporal trajectory of stem cells transplanted to hindlimb ischemia mice, which demonstrates the great potential of CIONs-22 as MPI tracers to advance stem cell therapies.

Magnetic particle imaging (MPI) is a noninvasive tomographic imaging modality for the quantitative visualization of magnetic nanoparticles (MNPs) with high temporal and spatial resolution. The general capability of MPI for cell tracking (e.g., monitoring living cells labeled with MNPs) has successfully been shown. MNPs in cell culture media are often subjected to structural and magnetic changes. In addition to the deteriorating reproducibility, this also complicates the systematic study of the relationship between the MNP properties and their cellular uptake for MPI. Here, we present a method for the preparation of magnetically labeled THP-1 (Tamm–Horsfall Protein-1) monocytes that are used in MPI cell tracking. The method development was performed using two different MPI tracers, which exhibited electrostatic and steric stabilizations, respectively. In the first step, the interaction between the MNPs and cell culture media was investigated and adjusted to ensure high structural and magnetic stability. Furthermore, the influences of the incubation time, MNP concentration used for cellular uptake, and individual preparation steps (e.g., the washing of cells) were systematically investigated. Finally, the success of the developed loading method was demonstrated by the MPI measurements. The presented systematic investigation of the factors that influence the MNP loading of cells will help to develop a reliable and reproducible method for MPI monocyte tracking for the early detection of inflammation in the future.

Magnetic particle imaging (MPI) and fluorine-19 (19F) MRI produce images which allow for quantification of labeled cells. MPI is an emerging instrument for cell tracking, which is expected to have superior sensitivity compared to 19F MRI. Our objective is to assess the cellular sensitivity of MPI and 19F MRI for detection of mesenchymal stem cells (MSC) and breast cancer cells. Cells were labeled with ferucarbotran or perfluoropolyether, for imaging on a preclinical MPI system or 3 Tesla clinical MRI, respectively. Using the same imaging time, as few as 4000 MSC (76 ng iron) and 8000 breast cancer cells (74 ng iron) were reliably detected with MPI, and 256,000 MSC (9.01 × 1016 19F atoms) were detected with 19F MRI, with SNR > 5. MPI has the potential to be more sensitive than 19F MRI for cell tracking. In vivo sensitivity with MPI and 19F MRI was evaluated by imaging MSC that were administered by different routes. In vivo imaging revealed reduced sensitivity compared to ex vivo cell pellets of the same cell number. We attribute reduced MPI and 19F MRI cell detection in vivo to the effect of cell dispersion among other factors, which are described.

No abstract available

Magnetic particle imaging (MPI) provides hotspot tracking and direct quantification of superparamagnetic iron oxide nanoparticle (SPIO)-labelled cells. Bioluminescence imaging (BLI) with the luciferase reporter gene Akaluc can provide complementary information on cell viability. Thus, we explored combining these technologies to provide a more holistic view of cancer cell fate in mice. Akaluc-expressing 4T1Br5 cells were labelled with the SPIO Synomag-D and injected into the mammary fat pads (MFP) of four nude mice. BLI was performed on days 0, 6 and 13, and MPI was performed on days 1, 8 and 14. Ex vivo histology and fluorescence microscopy of MFP and a potential metastatic site was conducted. The BLI signal in the MFP increased significantly from day 0 to day 13 (p < 0.05), mirroring tumor growth. The MPI signal significantly decreased from day 1 to day 14 (p < 0.05) due to SPIO dilution in proliferating cells. Both modalities detected secondary metastases; however, they were visualized in different anatomical regions. Akaluc BLI complemented MPI cell tracking, allowing for longitudinal measures of cell viability and sensitive detection of distant metastases at different locations. We predict this multimodal imaging approach will help to evaluate novel therapeutics and give a better understanding of metastatic mechanisms.

Iron oxides nanoparticles tailored for magnetic particle imaging (MPI) have been synthesized, and their MPI signal intensity is 3 times of commercial MPI contrast (Ferucarbotran, also called Vivotrax) and 7 times of MRI contrast (Feraheme) at the same Fe concentration. MPI tailored iron oxide nanoparticles were encapsulated with semiconducting polymers to produce Janus nanoparticles that possessed optical and magnetic properties for MPI and fluorescence imaging. Janus particles were applied to cancer cell labeling and in vivo tracking, and as few as 250 cells were imaged by MPI after implantation, corresponding to an amount of 7.8 ng of Fe. Comparison with MRI and fluorescence imaging further demonstrated the advantages of our Janus particles for MPI—super sensitivity, unlimited tissue penetration and linear quantitativity.

Many labs have been developing cellular magnetic resonance imaging (MRI), using both superparamagnetic iron oxide nanoparticles (SPIONs) and fluorine-19 (19F)-based cell labels, to track immune and stem cells used for cellular therapies. Although SPION-based MRI cell tracking has very high sensitivity for cell detection, SPIONs are indirectly detected owing to relaxation effects on protons, producing negative magnetic resonance contrast with low signal specificity. Therefore, it is not possible to reliably quantify the local tissue concentration of SPION particles, and cell number cannot be determined. 19F-based cell tracking has high specificity for perfluorocarbon-labeled cells, and 19F signal is directly related to cell number. However, 19F MRI has low sensitivity. Magnetic particle imaging (MPI) is a new imaging modality that directly detects SPIONs. SPION-based cell tracking using MPI displays great potential for overcoming the challenges of MRI-based cell tracking, allowing for both high cellular sensitivity and specificity, and quantification of SPION-labeled cell number. Here we describe nanoparticle and MPI system factors that influence MPI sensitivity and resolution, quantification methods, and give our perspective on testing and applying MPI for cell tracking.

Adoptive cellular therapy (ACT) is a potent strategy to boost the immune response against cancer. ACT is effective against blood cancers but faces challenges in treating solid tumors. A critical step for the success of ACT immunotherapy is to achieve efficient trafficking and persistence of T cells to solid tumors. Non-invasive tracking of the accumulation of adoptively transferred T cells to tumors would greatly accelerate development of more effective ACT strategies. We demonstrate the use of magnetic particle imaging (MPI) to non-invasively track ACT T cells in vivo in a mouse model of brain cancer. Magnetic labeling did not impair primary tumor-specific T cells in vitro, and MPI allowed the detection of labeled T cells in the brain after intravenous or intracerebroventricular administration. These results support the use of MPI to track adoptively transferred T cells and accelerate the development of ACT treatments for brain tumors and other cancers.

Magnetic particle imaging (MPI) is an emerging ionizing radiation-free biomedical tracer imaging technique that directly images the intense magnetization of superparamagnetic iron oxide nanoparticles (SPIOs). MPI offers ideal image contrast because MPI shows zero signal from background tissues. Moreover, there is zero attenuation of the signal with depth in tissue, allowing for imaging deep inside the body quantitatively at any location. Recent work has demonstrated the potential of MPI for robust, sensitive vascular imaging and cell tracking with high contrast and dose-limited sensitivity comparable to nuclear medicine. To foster future applications in MPI, this new biomedical imaging field is welcoming researchers with expertise in imaging physics, magnetic nanoparticle synthesis and functionalization, nanoscale physics, and small animal imaging applications.

The therapeutic potential of mesenchymal stem cells (MSCs) is limited, as many cells undergo apoptosis following administration. In addition, the attraction of immune cells (predominately macrophages) to the site of implantation can lead to MSC rejection. We implemented a trimodal imaging technique to monitor the fate of transplanted MSCs and infiltrating macrophages in vivo. MSCs were labeled with an iron oxide nanoparticle (ferumoxytol) and then implanted within the hind limb muscle of 10 C57BI/6 mice. Controls received unlabeled MSCs (n = 5). A perfluorocarbon agent was administered intravenously for uptake by phagocytic macrophages in situ; 1 and 12 days later, the ferumoxytol-labeled MSCs were detected by proton (1H) magnetic resonance imaging (MRI) and magnetic particle imaging (MPI). Perfluorocarbon-labeled macrophages were detected by fluorine-19 (19F) MRI. 1H/19F MRI was acquired on a clinical scanner (3 T) using a dual-tuned surface coil and balanced steady-state free precession (bSSFP) sequence. The measured volume of signal loss and MPI signal declined over 12 days, which is consistent with the death and clearance of iron-labeled MSCs. 19F signal persisted over 12 days, suggesting the continuous infiltration of perfluorocarbon-labeled macrophages. Because MPI and 19F MRI signals are directly quantitative, we calculated estimates of the number of MSCs and macrophages present over time. The presence of MSCs and macrophages was validated with histology following the last imaging session. This is the first study to combine the use of iron- and fluorine-based MRI with MPI cell tracking.

Introduction Magnetic particle imaging (MPI) is a new imaging modality that sensitively and specifically detects superparamagnetic iron oxide nanoparticles (SPIONs) within a sample. SPION-based MRI cell tracking has very high sensitivity, but low specificity and quantification of iron labeled cells is difficult. MPI cell tracking could overcome these challenges. Methods MDM-AB-231BR cells labeled with MPIO, mice were intracardially injected with either 2.5 × 105 or 5.0 × 105 cells. MRI was performed in vivo the same day at 3T using a bSSFP sequence. After mice were imaged ex vivo with MPI. In a second experiment Mice received an intracardiac injection of either 2.5 × 10 5 or 5 × 10 4 MPIO-labeled 231BR cells. In a third experiment, mice were injected with 5 × 10 4 4T1BR cells, labelled with either MPIO or the SPION Vivotrax. MRI and MPI was performed in vivo. Results Signal from MPI and signal voids from MRI both showed more iron content in mice receiving an injection of 5.0 × 105 cells than the 2.5 × 105 injection. In the second experiment, Day 0 MRI showed signal voids and MPI signal was detected in all mouse brains. The MPI signal and iron content measured in the brains of mice that were injected with 2.5 × 10 5 cells were approximately four times greater than in brains injected with 5 × 10 4 cells. In the third experiment, in vivo MRI was able to detect signal voids in the brains of mice injected with Vivotrax and MPIO, although voids were fainter in Vivotrax labeled cells. In vivo MPI signal was only detectable in mice injected with MPIO-labeled cells. Conclusion This is the first example of the use of MPIO for cell tracking with MPI. With an intracardiac cell injection, approximately 15% of the injected cells are expected to arrest in the brain vasculature. For our lowest cell injection of 5.0 × 104 cells this is ∼10000 cells.

Pulmonary fibrosis (PF) is a progressive and chronic lung disease characterized by repeated alveolar epithelial injury that leads to excessive extracellular matrix deposition, resulting in tissue thickening, scarring, and impaired gas exchange, leading to respiratory dysfunction. In the United States, around 50,000 new cases are reported annually, with patients facing serious complications such as pneumothorax, pulmonary hypertension, respiratory failure, and an increased risk of lung cancer. Current therapeutic options are limited in efficacy and primarily aim to slow disease progression. Cell therapy has emerged as a promising intervention, offering the potential to regenerate damaged lung tissue, modulate inflammation, and improve pulmonary function. However, the effectiveness of these therapies depends significantly on the ability to monitor the distribution, survival, and integration of transplanted cells within the host lungs. Magnetic Particle Imaging (MPI) is a novel, non-invasive, preclinical imaging modality that utilizes superparamagnetic iron oxide nanoparticles (SPIONs) as tracers. MPI offers high sensitivity, specificity, and no background signal, allowing for real-time and quantitative tracking of labeled cells in vivo. In this study, we investigated the use of MPI for monitoring human distal lung epithelial progenitor cells transplanted into the lungs of immunocompromised mice. Cells were labeled with varying SPION concentrations to optimize the signal, confirmed by immunostaining and iron quantification. After intratracheal instillation, 2D MPI scans were acquired to track the spatial distribution of transplanted cells. Longitudinal imaging over 2 weeks enabled visualization of cell integration and retention within lung tissue. Successful instillation exhibited MPI signals in both left and right lungs, which decreased (~65%) over time. Mice were subsequently sacrificed for histological validation. This study demonstrates the utility of MPI for noninvasive, longitudinal tracking of cell therapy in pulmonary fibrosis, and pivots around the intricate techniques utilized during the procedures.

Adoptive cellular therapy (ACT) is a potent strategy to boost the immune response against cancer. ACT is an effective treatment for blood cancers, such as leukemias and lymphomas, but faces challenges treating solid tumors and cancers in locations like the brain. A critical step for success of ACT immunotherapy is achieving efficient trafficking of T cells to solid tumors, and the non-invasive and quantitative tracking of adoptively transferred T cell biodistribution would accelerate its development. Here, we demonstrate the use of Magnetic Particle Imaging (MPI) to non-invasively track ACT T cells in vivo. Labeling T cells with the superparamagnetic iron oxide nanoparticle tracer ferucarbotran did not affect T cell viability, phenotype, or cytotoxic function in vitro. Following ACT, ferucarbotran-labeled T cells were detected and quantified using MPI ex vivo and in vivo, in a mouse model of invasive brain cancer. Proof-of-principle in vivo MPI demonstrated its capacity to detect labeled T cells in lungs and liver after intravenous administration and to monitor T cell localization in the brain after intraventricular administration. Ex vivo imaging using MPI and optical imaging suggests accumulation of systemically administered ferucarbotran-labeled T cells in the brain, where MPI signal from ferucarbotran tracers and fluorescently tagged T cells were observed. Ex vivo imaging also suggest differential accumulation of nanoparticles and viable T cells in other organs like the spleen and liver. These results support the use of MPI to track adoptively transferred T cells and accelerate the development of ACT treatments for brain tumors and other cancers.

No abstract available

Magnetic particle imaging (MPI) is an emerging biomedical imaging modality that enables non-invasive quantification of superparamagnetic iron oxide nanoparticle (SPION) tracers by comparing signal in a region of interest (ROI) to that of reference samples of known tracer mass. However, despite growing interest and use of MPI, recent studies have raised concern over quantification accuracy as a function of the volume over which the tracer is distributed. Here, a constant mass dilution series of the tracer Ferucarbotran contained in well-defined sample geometries was used to evaluate the effect of relative reference and object sample volumes on MPI quantification accuracy. MPI images of this series were acquired under various scan modes, segmented using two commonly used methods, and iron mass quantified relative to two reference sample sets with different volumes. The maximum signal and percent error relative to ground truth were compared amongst these sample volumes, scan modes, analysis methods, and reference volumes. Tracer concentration influenced signal distribution and, in some cases, influenced iron estimation accuracy. Reference samples that were similar in volume to the volume of interest were found to provide the most accurate results across scan modes. The observed behavior is attributed to the partial volume effect, which arises due to the finite resolution in MPI. These results are discussed within the current scope of the field and suggest that care must be taken when selecting reference sample volume relative to test sample volume for quantitative MPI studies.

Objective. Many applications of magnetic nanoparticles (MNPs) require accumulation at target organs that can be challenging to achieve and to characterize. Magnetic particle imaging (MPI) enables sensitive and quantitative detection of MNPs, however the proximity of target signals to high accumulation organs hinders accurate quantification due to signal spillover effects. Specifically, we sought to systematically evaluate MPI quantification strategies under conditions mimicking pre-clinical animal studies and account for spillover. Approach. We developed an anatomically accurate 3D-printed mouse phantom with a fillable liver cavity, brain and lung ports and a hind flank cavity. By emulating high liver MNP uptake alongside low MNP concentrations in target locations at varying distances, we compared quantification from threshold-based and constant-volume segmentations, as well as a subtraction approach, both for 2D and 3D MPI scans. Main results. Thresholding was reliable for isolated high-signal regions but overestimated signal near the liver or at low signal-to-noise ratios. Constant-volume segmentation improved signal separation, and subtraction strategies mitigated spillover overestimation, enhancing the limit of quantification. With the subtraction approach we were able to quantify as low a signal as 0.05 and 0.25% of the total dose in the phantom in the brain/lung and hind flank of the mouse phantom with 3D MPI, respectively. Significance. These results underscore the importance of accounting for superimposed signals in quantitative MPI and highlight anatomically correct phantoms as essential tools for refining biodistribution assessment methods in nanomedicine using MPI.

Magnetic Particle Imaging (MPI) is a powerful technique for non-invasive imaging and iron quantification using superparamagnetic iron oxide nanoparticles (SPIONs), with applications ranging from in vivo cell tracking to tracer distribution and biodistribution studies. As the MPI community continues to grow and diversify, there is an increasing recognition of the need for standardized approaches in signal quantification to ensure reproducibility, comparability, and reliable interpretation of results across studies. A key area where standardization is particularly needed is in the construction of calibration curves for quantitative MPI. In this study, we demonstrate that calibration curves derived from SPIONs in solution differ markedly from those obtained in cellular environments. We therefore propose calibrating MPI signal against the number of labelled cells, a strategy that accounts for altered SPION behaviours in the cellular environment and enables more accurate estimation of intracellular iron content. Another critical but often overlooked factor in MPI quantification is the influence of SPION concentration and spatial distribution within the sample. We show that even modest variations in concentration can significantly affect MPI signal intensity, challenging the commonly assumed linear relationship between signal and iron content. Our findings reveal that variations in concentration can introduce nonlinearities in signal response, thereby altering calibration curves and impacting the accuracy and reproducibility of MPI-based quantification. By systematically examining the effects of environmental context and SPION concentration, our study provides a framework for biologically relevant MPI calibration strategies and supports the development of more standardized, reproducible quantification protocols.

Clinical adoption of NK cell immunotherapy is underway for medulloblastoma and osteosarcoma, however there is currently little feedback on cell fate after administration. We propose magnetic particle imaging (MPI) may have applications for the quantitative detection of NK cells. Human-derived NK-92 cells were labeled by co-incubation with iron oxide nanoparticles (VivoTrax™) for 24 h then excess nanoparticles were washed with centrifugation. Cytolytic activity of labeled versus unlabeled NK-92 cells was assessed after 4 h of co-incubation with medulloblastoma cells (DAOY) or osteosarcoma cells (LM7 or OS17). Labeled NK-92 cells at two different doses (0.5 or 1 × 106) were administered to excised mouse brains (cerebellum), fibulas, and lungs then imaged by 3D preclinical MPI (MOMENTUM™) for detection relative to fiducial markers. NK-92 cells were also imaged by clinical-scale MPI under development at Magnetic Insight Inc. NK-92 cells were labeled with an average of 3.17 pg Fe/cell with no measurable effects on cell viability or cytolytic activity against 3 tumor cell lines. MPI signal was directly quantitative with the number of labeled NK-92 cells, with preclinical limit of detection of 3.1 × 104 cells on MOMENTUM imager. Labeled NK-92 cells could be accurately localized in mouse brains, fibulas, and lungs within < 1 mm of stereotactic injection coordinates with preclinical scanner. Feasibility for detection on a clinical-scale MPI scanner was demonstrated using 4 × 107 labeled NK-92 cells, which is in the range of NK cell doses administered in our previous clinical trial. MPI can provide sensitive, quantitative, and accurate spatial information on NK cells soon after delivery, showing initial promise to address a significant unmet clinical need to track NK cell fate in patients.

Tumour Associated Macrophages (TAMs) play a crucial role in breast cancer progression and have the potential to be used as a biomarker for patient prognosis. Magnetic particle imaging (MPI) is an emerging modality which can detect cells labelled with superparamagnetic iron oxide (SPIO) nanoparticles and can be used for non-invasive TAM assessment. However, MPI TAM detection is limited by its effective dynamic range. This limitation occurs when SPIO nanoparticles injected intravenously accumulate in the liver resulting in a large MPI signal which shadows regions of interest with lower signals (i.e the tumour) preventing their isolation and quantification. In this study we test an advanced reconstruction algorithm which allows us to prescribe a small focused field of view (FOV) on lower signals of interest. We then demonstrate the success of this method with an in vivo tumour model and show enhanced image quality and successful quantification of TAMs in mouse mammary tumours with different metastatic potentials (4T1 and E0771). Utilizing in vivo MPI, we did not see significant differences in the MPI signal for 4T1 tumours compared to E0771. These findings highlight the potential of MPI for in vivo TAM quantification offering a promising avenue for broader applications in cancer research and potentially overcoming constraints of MPI in other in vivo imaging contexts.

No abstract available

The strengths of Magnetic Particle Imaging (MPI) lay in its sensitivity, quantitative nature, and lack of signal attenuation for Superparamagnetic Iron Oxide Nanoparticles (SPION). These advantages make MPI a powerful tool for the non-invasive monitoring of tracer behaviour over time. With more MPI studies emerging, a standardized method for determining the boundaries of a region of interest (ROI) and iron quantification is crucial. The current approaches are inconsistent, making it challenging to compare studies, hindering MPI progression. Here we showcase three different ROI selection methods for the quantification of iron in vivo and ex vivo. Healthy mice were intravenously administered a long circulating tracer, never before applied in MPI, and the ROI methods were tested for their ability to accurately quantify the total signal present, in addition to the accumulation of the tracer in individual organs. We discuss how the quantified iron amount can be vastly altered based on the choice of ROI, the importance of the standard curve and the challenges associated with each method. Lastly, the user variability and accuracy of each method was compared by 3 independent users to ensure their consistency and lack of bias.

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Purpose Endovascular stents are medical devices, which are implanted in stenosed blood vessels to ensure sufficient blood flow. Due to a high rate of in-stent re-stenoses, there is the need of a noninvasive imaging method for the early detection of stent occlusion. The evaluation of the stent lumen with computed tomography (CT) and magnetic resonance imaging (MRI) is limited by material-induced artifacts. The purpose of this work is to investigate the potential of the tracer-based modality magnetic particle imaging (MPI) for stent lumen visualization and quantification. Methods In this in vitro study, 21 endovascular stents were investigated in a preclinical MPI scanner. Therefore, the stents were implanted in vessel phantoms. For the signal analysis, the phantoms were scanned without tracer material, and the signal-to-noise-ratio was analyzed. For the evaluation of potential artifacts and the lumen quantification, the phantoms were filled with diluted tracer agent. To calculate the stent lumen diameter a calibrated threshold value was applied. Results We can show that it is possible to visualize the lumen of a variety of endovascular stents without material induced artifacts, as the stents do not generate sufficient signals in MPI. The stent lumen quantification showed a direct correlation between the calculated and nominal diameter (r = 0.98). Conclusion In contrast to MRI and CT, MPI is able to visualize and quantify stent lumina very accurately.

Changes in blood flow velocity play a crucial role during pathogenesis and progression of cardiovascular diseases. Imaging techniques capable of assessing flow velocities are clinically applied but are often not accurate, quantitative, and reliable enough to assess fine changes indicating the early onset of diseases and their conversion into a symptomatic stage. Magnetic particle imaging (MPI) promises to overcome these limitations. Existing MPI-based techniques perform velocity estimation on the reconstructed images, which restricts the measurable velocity range. Therefore, we developed a novel velocity quantification method by adapting the Doppler principle to MPI. Our method exploits the velocity-dependent frequency shift caused by a tracer motion-induced modulation of the emitted signal. The fundamental theory of our method is deduced and validated by simulations and measurements of moving phantoms. Overall, our method enables robust velocity quantification within milliseconds, with high accuracy, no radiation risk, no depth-dependency, and extended range compared to existing MPI-based velocity quantification techniques, highlighting the potential of our method as future medical application.

Lipids are a major source of energy for most tissues, and lipid uptake and storage is therefore crucial for energy homeostasis. So far, quantification of lipid uptake in vivo has primarily relied on radioactive isotope labeling, exposing human subjects or experimental animals to ionizing radiation. Here, we describe the quantification of in vivo uptake of chylomicrons, the primary carriers of dietary lipids, in metabolically active tissues using magnetic particle imaging (MPI) and magnetic particle spectroscopy (MPS). We show that loading artificial chylomicrons (ACM) with iron oxide nanoparticles (IONPs) enables rapid and highly sensitive post hoc detection of lipid uptake in situ using MPS. Importantly, by utilizing highly magnetic Zn-doped iron oxide nanoparticles (ZnMNPs), we generated ACM with MPI tracer properties superseding the current gold-standard, Resovist, enabling quantification of lipid uptake from whole-animal scans. We focused on brown adipose tissue (BAT), which dissipates heat and can consume a large part of nutrient lipids, as a model for tightly regulated and inducible lipid uptake. High BAT activity in humans correlates with leanness and improved cardiometabolic health. However, the lack of nonradioactive imaging techniques is an important hurdle for the development of BAT-centered therapies for metabolic diseases such as obesity and type 2 diabetes. Comparison of MPI measurements with iron quantification by inductively coupled plasma mass spectrometry revealed that MPI rivals the performance of this highly sensitive technique. Our results represent radioactivity-free quantification of lipid uptake in metabolically active tissues such as BAT.

Immunotherapies, such as dendritic cell- (DC-)based therapies, are useful for treating cancer as an alternative to or in combination with traditional therapies. Cells must migrate to lymphoid organs to be effective and the magnitude of the ensuing T cell response is proportional to the number of lymph node-migrated DC. With less than 10% of cells expected to reach their destination, there is a need for an imaging modality capable of sensitively and quantitatively detecting cells. MRI has been used to track DC using iron and 19F methods, with limitations. Quantification of iron-induced signal loss is indirect and challenging; 19F signal is directly quantifiable but lacks sensitivity. Magnetic Particle Imaging (MPI) directly detects superparamagnetic iron oxide nanoparticles (SPIO) and enables quantitation of low numbers of SPIO-labeled cells. Here we describe the first study using MPI to track and quantify the migration of DC, injected into the footpads of C57BL/6 mice, to the popliteal lymph nodes (pLNs). As DC migrate from the site of injection to the lymph nodes, we measured a decrease in signal in the footpads and an increase in signal at the pLNs. The presence of SPIO-labeled DC in nodes was validated by ex vivo MPI and histology. By measuring the iron mass per cell in samples of labeled cells, we were able to provide an estimate of cell number for each source of signal and we report a sensitivity of approximately 4000 cells in vivo and 2000 cells ex vivo. For some mice, MPI was compared to cellular MRI. We also bring attention to the issue of resolving unequal signals within close proximity, a challenge for many pre-clinical studies using a highly concentrated tracer bolus that over shadows nearby lower signals. This study demonstrates the clear advantage of MPI to detect and quantify cells in vivo, bridging the gap left by cellular MRI, and all other in vivo imaging modalities, and opening the door for quantitative imaging of cellular immunotherapies.

Objective. Magnetic particle imaging (MPI) is an emerging tomographic ‘hot spot’ imaging modality with potential to visualize superparamagnetic iron oxide nanoparticle tracer distributions with high sensitivity and quantitative accuracy. MPI shares many similarities with positron emission tomography (PET), where the partial volume effect (PVE) can result in signal under- and over-quantification due to spill-over of signal arising from limited resolution. While the PVE has been alluded to in the MPI literature it has not been previously studied nor characterized. The objective of this study was to systematically characterize this PVE in MPI. Approach. This contribution characterizes the PVE using models of varying size and shape filled with a uniform concentration of tracer. The effect of object size on signal distribution was analyzed after application of a new image post-processing filter. Main results. As object size increased, signal distribution increased to a maximum signal value independent of object geometry and proportional to tracer concentration. Furthermore, for small objects with characteristic dimensions below the resolution of the tracer at the scanning conditions used, signal suppression was observed. These results are consistent with foundational observations of PVE in PET, suggesting that approaches to overcome the PVE in PET may be applicable to MPI. Significance. This finding has significant impact on the MPI field by demonstrating the presence of the PVE phenomenon that can directly influence imaging results.

Magnetic Particle Imaging (MPI) is a cutting-edge noninvasive imaging technique that offers high sensitivity, quantitative accuracy, and operates without the need for ionizing radiation compared to other imaging techniques. Utilizing superparamagnetic iron oxide nanoparticles (SPIONs) as tracers, MPI enables direct and precise visualization of target sites with no limitation on imaging depth. Unlike magnetic resonance imaging (MRI), which relies on uniform magnetic fields to produce anatomical images, MPI enables direct, background-free visualization and quantification of SPIONS within living organisms. This article provides an in-depth overview of MPI’s applications in tracking tumor development and supporting cancer therapy. The distinct physical principles that underpin MPI, including its ability to produce high-contrast images devoid of background tissue interference, facilitating accurate tumor identification and real-time monitoring of treatment outcomes, are outlined. The review outlines MPI’s advantages over conventional imaging techniques in terms of sensitivity and resolution, and examines its capabilities in visualizing tumor vasculature, tracking cellular movement, evaluating inflammation, and conducting magnetic hyperthermia treatments. Recent progress in tracer optimization and magnetic navigation has expanded MPI’s potential for targeted drug delivery, along with deep machine learning procedures for MPI applications. Additionally, considerations around safety and the feasibility of clinical implementation are also discussed in the present review. Overall, MPI is positioned as a promising tool in advancing cancer diagnostics, personalized therapy assessment, and noninvasive treatment strategies.

Abstract Magnetic particle imaging (MPI) is a promising imaging modality nearing clinical introduction. MPI's tracer‐based principle allows for highly sensitive background‐free imaging. Potential clinical applications include cardiovascular imaging and endovascular interventions. In principle, medical instruments are invisible in MPI due to the missing signal generation. Therefore, permanent marking technologies have been introduced. Additionally, temporary markers are of interest for follow‐up examinations after stent implantation to prevent artifacts during postinterventional stent lumen quantification. Consequently, medical instrument markers for MPI, based on biodegradable polylactic acid (PLA) and superparamagnetic iron‐oxide nanoparticles (SPIONs), are developed in this study. To investigate the markers, signal characteristics and degradation over time are studied for 28 d in a water bath at 37 °C. The samples are analyzed using a scale, micro‐CT, microscopy, magnetic particle spectroscopy (MPS), MPI, and vibrating sample magnetometry (VSM). A continuous mass decrease is detected (≈90% after 28 d), while MPS and MPI data show no loss of signal. VSM confirms that the markers’ mass reduction can be accounted for the degradation of PLA, while the SPIONs hardly detach from the coating. The introduced marking technology, with its degradation characteristics and signal behavior, is the basis for a variety of anticipated medical application scenarios.

Magnetic particle imaging (MPI) is an imaging technique that quantitatively visualizes the spatial distribution of magnetic nanoparticles (MNPs). Multicolor MPI allows for the separation of signals acquired simultaneously from various types of MNPs into different color channels, which has great potential for biomedical applications. However, due to the similarity of magnetic response of different MNPs, the different color channels have leakage to each other, causing low quantification accuracy. Thus, it is of great importance to completely separate different color channels for different MNPs to achieve quantitative color imaging. In this study, we propose a novel single-harmonic-based dual-color MPI approach to quantitatively visualize the spatial distribution of two different MNPs. The mathematical model of dual-color MPI is derived with single-harmonic-based narrowband MPI. The 3rd harmonic of the MNP magnetization is measured for image reconstruction. Numerical simulation is performed to evaluate the feasibility of the proposed approach with different noise levels. Furthermore, phantom experiments are performed with two different types of commercial MNPs. Experimental results show that the iron content of two types of MNPs versus the volume percentage of samples has a linearity of better than 0.98. It demonstrates that the proposed approach allows for quantitative dual-color imaging of the two different types of MNPs.

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.

Abstract Quantification and imaging of magnetic nanoparticles is of vital importance for various novel biomedical applications, like cell tracking, drug targeting or hyperthermia treatments. In this work we studied the performance of magnetic resonance imaging (MRI) and magnetic particle imaging (MPI) for quantitative imaging of magnetic nanoparticles (MNP). This was done by measurements of serial dilutions of MNP (Ferucarbotran) in two different media (water and CuSO4 solution). The concentration range in which quantification was possible was determined for each technique, and the influence of the environment was analyzed and discussed. This revealed a significantly stronger influence of the surrounding medium on MRI performance as compared to MPI. All results were validated by measurements using their respective zero-dimensional (spectroscopic) techniques nuclear magnetic resonance and magnetic particle spectroscopy, showing similar behavior compared to the imaging modalities. Physical explanations of all observed effects are given, and a concentration range is determined in which the advantages of both imaging techniques can be utilized.

Pulmonary delivery of therapeutics is attractive due to rapid absorption and non-invasiveness but it is challenging to monitor and quantify the delivered aerosol or powder. Currently, single-photon emission computed tomography (SPECT) is used but requires inhalation of radioactive labels that typically have to be synthesized and attached by hot chemistry techniques just prior to every scan. Methods: In this work, we demonstrate that superparamagnetic iron oxide nanoparticles (SPIONs) can be used to label and track aerosols in vivo with high sensitivity using an emerging medical imaging technique known as magnetic particle imaging (MPI). We perform proof-of-concept experiments with SPIONs for various lung applications such as evaluation of efficiency and uniformity of aerosol delivery, tracking of the initial aerosolized therapeutic deposition in vivo, and finally, sensitive visualization of the entire mucociliary clearance pathway from the lung up to the epiglottis and down the gastrointestinal tract to be excreted. Results: Imaging of SPIONs in the lung has previously been limited by difficulty of lung imaging with magnetic resonance imaging (MRI). In our results, MPI enabled SPION lung imaging with high sensitivity, and a key implication is the potential combination with magnetic actuation or hyperthermia for MPI-guided therapy in the lung with SPIONs. Conclusion: This work shows how magnetic particle imaging can be enabling for new imaging and therapeutic applications of SPIONs in the lung.

Objective: Magnetic particle imaging (MPI) is a new, fast 3D imaging technique, which is considered promising for angiographies. As available MPI scanners suffer from restricted spatial resolution and are mostly constructed for small animal imaging, no vessels within one organ have been depicted by MPI, yet. The purpose of this study was to develop an ex vivo organ perfusion system to display vessels within one organ of human size by MPI and to compare the results to an established 3D imaging technique. Approach: An ex vivo porcine kidney perfusion system compatible with digital subtraction angiography (DSA), magnetic resonance tomography and MPI was developed. DSA was used to exemplarily prove intact vessel structures under ex vivo perfusion in two organs. Perfusion in nine organs was displayed by the 3D imaging techniques magnetic resonance angiography (MRA) and MPI angiography. All visible vessels in MRA and MPI were counted and their number compared between both techniques. Main results: The ex vivo organ perfusion system allowed us to perform angiographies by DSA, MRA and MPI. With it, organs of human size could be imaged in small animal scanners, which permitted us to depict vessels within one organ by MPI for the first time. In comparison to MRA, 33% of all vessels were visible in MPI, a difference probably caused by restricted spatial resolution in MPI. Significance: The presented ex vivo organ perfusion system can serve to practically evaluate MPI’s potential for angiography in human-sized organs. This is especially relevant as long as available, for angiography-suited MPI scanners still suffer from size and spatial resolution restrictions.

Magnetic Particle Imaging (MPI) is a new imaging modality, which maps the distribution of magnetic nanoparticles (MNP) in 3D with high temporal resolution. It thus may be suited for cardiovascular imaging. Its sensitivity and spatial resolution critically depend on the magnetic properties of MNP. Therefore, we used novel multicore nanoparticles (MCP 3) for in-vivo MPI in rats and analyzed dose requirements, sensitivity and detail resolution. 8 rats were examined using a preclinical MPI scanner (Bruker Biospin GmbH, Germany) equipped with a separate receive coil. MCP 3 and Resovist were administered intravenously (i.v.) into the rats’ tail veins at doses of 0.1, 0.05 and 0.025 mmol Fe/kg followed by serial MPI acquisition with a temporal resolution of 46 volumes per second. Based on a qualitative visual scoring system MCP 3–MPI images showed a significantly (P ≤ 0.05) higher image quality than Resovist-MPI images. Morphological features such as vessel lumen diameters (DL) of the inferior vena cava (IVC) and abdominal aorta (AA) could be assessed along a 2-cm segment in mesenteric area only after administration of MCP 3 at dosages of 0.1, 0.05 mmol Fe/kg. The mean DL ± SD estimated was 2.7 ± 0.6 mm for IVC and 2.4 ± 0.7 mm for AA. Evaluation of DL of the IVC and AA was not possible in Resovist-MPI images. Our results show, that MCP 3 provide better image quality at a lower dosage than Resovist. MCP 3-MPI with a clinically acceptable dose of 0.05 mmol Fe/kg increased the visibility of vessel lumens compared to Resovist-based MPI towards possible detection of vascular abnormalities such as stenosis or aneurysms, in vivo.

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Motivation: Magnetic Particle Imaging (MPI) is a radiation-free imaging technique particularly appealing for clinical applications, where reducing radiation exposure is crucial. Goal(s): Investigating the feasibility of MPI for real-time vascular imaging and its potential as a clinically viable imaging tool. Approach: To simulate real-life conditions, a portable MPI scanner for human application in combination with an X-ray system was tested on human cadaveric specimens with continuous perfusion. Results: The results demonstrated reliable visualization of femoral arteries with high temporal resolution, underscoring MPI's potential as a safe, effective complement to traditional vascular imaging. This approach is particularly beneficial for interventional radiology. Impact: MPI offers a radiation-free alternative for real-time vascular imaging, improving patient safety, especially in sensitive populations. With its high-resolution, dynamic imaging capabilities the integration into clinical settings could reduce/remove ionizing radiation and associated risks in interventional radiology.

Magnetic particle imaging (MPI) is an emerging imaging modality that shows potential in tumor imaging, cell tracking, and angiography. It uses the signal generated from superparamagnetic iron oxide nanoparticles (SPIONs) with zero attenuation in tissue, showing excellent sensitivity and contrast. MPI resolution and sensitivity are dependent on the nonlinear dynamic magnetization of the SPION tracer and can be improved by tuning their magnetic properties. Doping SPIONs with manganese or zinc is an effective and biocompatible route to modify the magnetic properties of SPIONs. This study developed SPIONs doped with manganese or zinc as MPI tracers using flame spray pyrolysis (FSP), a highly scalable synthesis technique. The MPI performance was evaluated with a MOMENTUM imager. Postsynthesis citrate coating and filtration significantly enhanced the MPI resolution of SPIONs. The Zn-doped SPIONs exhibited the best resolution, while Mn-doped SPIONs showed the highest sensitivity. The overall MPI performance of all tracers was closely linked to their magnetic diameter and susceptibility, but deviated noticeably from the predictions of the Langevin model. Zn-doped SPIONs were encapsulated in a water-dispersible nanocarrier using flash nanoprecipitation (FNP), circumventing the need for citrate coating while preserving MPI performance. These findings show that the hydrodynamic size, size distribution, and composition of the SPIONs are critical to MPI performance and highlight the potential of combining FSP and FNP for large-scale production of the MPI tracers.

Magnetic particle imaging (MPI) is a new imaging technique that utilizes biologically safe iron oxide nanoparticle tracers for medical imaging. Although in the preclinical stage, MPI has shown strong potential for applications such as cell tracking, angiography, cancer imaging, etc. With the ever-growing research interest in MPI and the increasing desire to apply it in clinical medical imaging, the design of tracers, MPI systems, image reconstruction algorithms, and other related components has become increasingly critical. To date, most of the theoretical studies in MPI rely on the static Langevin function to describe the magnetization responses of superparamagnetic iron oxide nanoparticle (SPION) tracers. However, under a fast-changing excitation field (usually tens of kHz), the magnetic relaxation time of SPION tracers is no longer negligible, thus, the static Langevin function is inaccurate in explaining the tracers’ magnetic signals in MPI. Herein, we apply a stochastic Langevin function with coupled Brownian–Néel relaxation models to study the dynamic magnetization responses of SPION tracers in MPI. The time domain magnetization responses (M–t curve), dynamic magnetization-field hysteresis loops (M–H curve), and point spread functions are modeled for different SPION tracer designs with varying physical and magnetic properties. Our results show that larger magnetic core sizesreduce the MPI spatial resolution. Conversely, thicker non-magnetic coatings on tracers do not significantly affect the spatial resolution. The increased anisotropy diminishes the MPI resolution, and a higher saturation magnetization favors higher MPI resolution.

Magnetic particle imaging (MPI), as an emerging radiation-free, high-sensitivity molecular imaging technique, holds broad application prospects in fields such as medical diagnosis, angiography, and targeted drug tracking. However, traditional three-dimensional MPI reconstruction algorithms face a problem in balancing reconstruction speed and image resolution. A hybrid reconstruction algorithm (Full Hybrid) based on spatial density constraints and residual iterative optimization is proposed in this work. This paper simulates Lissajous trajectory scanning and the non-linear response of magnetic particles based on the three-dimensional MPI simulation framework. The proposed hybrid method first utilizes the X-space method to obtain a basic spatial prior, then introduces field-free point (FFP) trajectory density to impose spatial weighting constraints on the reconstructed image. Experimental results demonstrated that this hybrid algorithm performs better in the reconstruction of complex three-dimensional topological structures (an H-shaped phantom). Comprehensive evaluation demonstrated that the reconstructed outputs reach a peak signal-to-noise ratio (PSNR) of 12.85 dB, a structural similarity index measure (SSIM) of 0.7321, and a root mean square error (RMSE) of 0.2278. Ablation experiments and comparison experiments further reinforced the advantages of the proposed method. These results demonstrate the numerical feasibility of the proposed reconstruction method for a three-dimensional phantom and provide a basis for further evaluation under multiple simulation conditions and real-scanner measurements.

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.

Abdominal aortic aneurysms (AAAs) are currently one of the leading causes of death in developed countries. Inflammation is crucial in the disease progression, having a substantial impact on various determinants in AAAs development. Magnetic particle imaging (MPI) is an innovative imaging modality, enabling the highly sensitive detection of magnetic nanoparticles (MNPs), suitable as surrogate marker for molecular targeting of vascular inflammation. For this study, Apolipoprotein E-deficient-mice underwent surgical implantation of osmotic minipumps with constant Angiotensin II infusion. After 3 and 4 weeks respectively, in-vivo-magnetic resonance imaging (MRI), ex-vivo-MPI and ex-vivo-magnetic particle spectroscopy (MPS) were performed. The results were validated by histological analysis, immunohistology and laser ablation-inductively coupled plasma-mass spectrometry. MR-angiography enabled the visualization of aneurysmal development and dilatation in the experimental group. A close correlation (R = 0.87) with histological area assessment was measured. Ex-vivo-MPS revealed abundant iron deposits in AAA samples and ex-vivo histopathology measurements were in good agreement (R = 0.76). Ex-vivo-MPI and MPS results correlated greatly (R = 0.99). CD68-immunohistology stain and Perls’-Prussian-Blue-stain confirmed the colocalization of macrophages and MNPs. This study demonstrates the feasibility of ex-vivo-MPI for detecting inflammation in AAA. The quantitative ability for mapping MNPs establishes MPI as a promising tool for monitoring inflammatory progression in AAA in an experimental setting.

Pulmonary embolism (PE), along with the closely related condition of deep vein thrombosis, affect an estimated 600 000 patients in the US per year. Untreated, PE carries a mortality rate of 30%. Because many patients experience mild or non-specific symptoms, imaging studies are necessary for definitive diagnosis of PE. Iodinated CT pulmonary angiography is recommended for most patients, while nuclear medicine-based ventilation/perfusion (V/Q) scans are reserved for patients in whom the use of iodine is contraindicated. Magnetic particle imaging (MPI) is an emerging tracer imaging modality with high image contrast (no tissue background signal) and sensitivity to superparamagnetic iron oxide (SPIO) tracer. Importantly, unlike CT or nuclear medicine, MPI uses no ionizing radiation. Further, MPI is not derived from magnetic resonance imaging (MRI); MPI directly images SPIO tracers via their strong electronic magnetization, enabling deep imaging of anatomy including within the lungs, which is very challenging with MRI. Here, the first high-contrast in vivo MPI lung perfusion images of rats are shown using a novel lung perfusion agent, MAA-SPIOs.

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Magnetic particle imaging (MPI) is an emerging tracer-based medical imaging modality that images non-radioactive, kidney-safe superparamagnetic iron oxide (SPIO) tracers. MPI offers quantitative, high-contrast and high-SNR images, so MPI has exceptional promise for applications such as cell tracking, angiography, brain perfusion, cancer detection, traumatic brain injury and pulmonary imaging. In assessing MPI’s utility for applications mentioned above, it is important to be able to assess tracer short-term biodistribution as well as long-term clearance from the body. Here, we describe the biodistribution and clearance for two commonly used tracers in MPI: Ferucarbotran (Meito Sangyo Co., Japan) and LS-oo8 (LodeSpin Labs, Seattle, WA). We successfully demonstrate that 3D MPI is able to quantitatively assess short-term biodistribution, as well as long-term tracking and clearance of these tracers in vivo.

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.

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