Pulsed MPI Relaxometry of Brownian and Néel Field-Dependent Relaxation in Superparamagnetic Magnetite Nanoparticles Confirm Theory and Simulations.

Superparamagnetic iron oxide nanoparticles (SPIOs) are used as tracers in Magnetic Particle Imaging (MPI). It is crucial to understand the magnetic properties of SPIOs for optimizing MPI imaging contrast, resolution, and sensitivity. Brownian and Néel relaxation theory developed in the early 1950s posits that relaxation times can vary with particle size, shell thickness, medium viscosity, and the applied field strength. Magnetic relaxation can soon provide a unique imaging capability, the ability to distinguish bound from unbound MPI tracers in vivo. Yet experimental validation of these theories has not been completed. In this paper, a novel method of pulsed magnetic field relaxometry is used to directly probe the relaxation behavior of superparamagnetic magnetite nanoparticles over a spectrum of magnetic field amplitudes, providing the first experimental validation of theoretical relaxation models. It is also shown that closed-form approximations generated in the early 1970s accurately match both data and numerical Fokker Planck computational models, which are computationally burdensome. This means researchers can trust these approximations for future modeling. All the findings can be translated to sinusoidal excitations used in conventional MPI scanning trajectories.

Transformer for low concentration image denoising in magnetic particle imaging.

Objective.Magnetic particle imaging (MPI) is an emerging tracer-basedin vivoimaging technology. The use of MPI at low superparamagnetic iron oxide nanoparticle concentrations has the potential to be a promising area of clinical application due to the inherent safety for humans. However, low tracer concentrations reduce the signal-to-noise ratio of the magnetization signal, leading to severe noise artifacts in the reconstructed MPI images. 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.Approach.Here, we propose a novel deep learning method for MPI image denoising and quality enhancing based on a sparse lightweight transformer model. The proposed residual-local transformer structure reduces model complexity to avoid overfitting, in which an information retention block facilitates feature extraction capabilities for the image details. Besides, we design a noisy concentration dataset to train our model. Then, we evaluate our method with both simulated and real MPI image data.Main results.Simulation experiment results show that our method can achieve the best performance compared with the existing deep learning methods for MPI image denoising. More importantly, our method is effectively performed on the real MPI image of samples with an Fe concentration down to 67µgFeml-1.Significance.Our method provides great potential for obtaining high quality MPI images at low concentrations.

Characterization of Microscopic Structures in Living Tumor by In Vivo Measurement of Magnetic Relaxation Time Distribution of Intratumor Magnetic Nanoparticles.

Tumor microscopic structure is crucial for determining properties such as cancer type, disease state (key for early diagnosis), and novel therapeutic strategies. Magnetic particle imaging is an early cancer diagnostic tool using magnetic nanoparticles as a tracer, which actualizes cancer theranostics in combination with hyperthermia treatment using the abilities of magnetic nanoparticles as a heat source. This study focuses on the microscopic structures associated with cancer cell distribution, the stromal compartment, and vascularization in different kinds of living tumors by analyzing the intratumor magnetic relaxation response of magnetic nanoparticles injected into the tumors. Furthermore, this study describes a sequential system for the measurement of magnetic relaxation time and analysis of the intratumor structure using nonbiological samples such as viscous fluids and solidified magnetic nanoparticles. Particularly, the fine discriminability achieved by reconstructing a distribution map representing the relationship between magnetic relaxation time and viscosity of medium is demonstrated, based on experimental data with a limited condition number. Observing tumor microscopic structure through the dynamic magnetization response of intratumor magnetic nanoparticles is a low-invasive tool for analyzing tumor tissue without dissection. It holds promise for the advancement of biomedical applications, such as early cancer theranostics, using magnetic nanoparticles.

Integration of photomagnetic bimodal imaging to monitor an autogenous exosome loaded platform: unveiling strong targeted retention effects for guiding the photothermal and magnetothermal therapy in a mouse prostate cancer model.

BACKGROUND: Prostate cancer (PCa) is the most prevalent cancer among males, emphasizing the critical need for precise diagnosis and treatment to enhance patient prognosis. Recent studies have extensively utilized urine exosomes from patients with cancer for targeted delivery. This study aimed to employ highly sensitive magnetic particle imaging (MPI) and fluorescence molecular imaging (FMI) to monitor the targeted delivery of an exosome-loaded platform at the tumour site, offering insights into a potential combined photothermal and magnetic thermal therapy regime for PCa. RESULTS: MPI and FMI were utilized to monitor the in vivo retention performance of exosomes in a prostate tumour mouse model. The exosome-loaded platform exhibited robust homologous targeting ability during imaging (SPIONs@EXO-Dye:66·48%±3·85%; Dye-SPIONs: 34·57%±7·55%, **P<0·01), as verified by in vitro imaging and in vitro tissue Prussian blue staining. CONCLUSIONS: The experimental data underscore the feasibility of using MPI for in vivo PCa imaging. Furthermore, the exosome-loaded platform may contribute to the precise diagnosis and treatment of PCa.

Advances in Vascular Diagnostics using Magnetic Particle Imaging (MPI) for Blood Circulation Assessment.

Rapid and accurate assessment of conditions characterized by altered blood flow, cardiac blood pooling, or internal bleeding is crucial for diagnosing and treating various clinical conditions. While widely used imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound offer unique diagnostic advantages, they fall short for specific indications due to limited penetration depth and prolonged acquisition times. Magnetic particle imaging (MPI), an emerging tracer-based technique, holds promise for blood circulation assessments, potentially overcoming existing limitations with reduction in background signals and high temporal and spatial resolution, below the millimeter scale. Successful imaging of blood pooling and impaired flow necessitates tracers with diverse circulation half-lives optimized for MPI signal generation. Recent MPI tracers show potential in imaging cardiovascular complications, vascular perforations, ischemia, and stroke. The impressive temporal resolution and penetration depth also position MPI as an excellent modality for real-time vessel perfusion imaging via functional MPI (fMPI). This review summarizes advancements in optimized MPI tracers for imaging blood circulation and analyzes the current state of pre-clinical applications. This work discusses perspectives on standardization required to transition MPI from a research endeavor to clinical implementation and explore additional clinical indications that may benefit from the unique capabilities of MPI.

Magnetic Particle Imaging-Guided Thermal Simulations for Magnetic Particle Hyperthermia.

Magnetic particle hyperthermia (MPH) enables the direct heating of solid tumors with alternating magnetic fields (AMFs). One challenge with MPH is the unknown particle distribution in tissue after injection. Magnetic particle imaging (MPI) can measure the nanoparticle content and distribution in tissue after delivery. The objective of this study was to develop a clinically translatable protocol that incorporates MPI data into finite element calculations for simulating tissue temperatures during MPH. To verify the protocol, we conducted MPH experiments in tumor-bearing mouse cadavers. Five 8-10-week-old female BALB/c mice bearing subcutaneous 4T1 tumors were anesthetized and received intratumor injections of Synomag®-S90 nanoparticles. Immediately following injection, the mice were euthanized and imaged, and the tumors were heated with an AMF. We used the Mimics Innovation Suite to create a 3D mesh of the tumor from micro-computerized tomography data and spatial index MPI to generate a scaled heating function for the heat transfer calculations. The processed imaging data were incorporated into a finite element solver, COMSOL Multiphysics®. The upper and lower bounds of the simulated tumor temperatures for all five cadavers demonstrated agreement with the experimental temperature measurements, thus verifying the protocol. These results demonstrate the utility of MPI to guide predictive thermal calculations for MPH treatment planning.

Dual-channel end-to-end network with prior knowledge embedding for improving spatial resolution of magnetic particle imaging.

Magnetic particle imaging (MPI) is an emerging non-invasive medical imaging tomography technology based on magnetic particles, with excellent imaging depth penetration, high sensitivity and contrast. Spatial resolution and signal-to-noise ratio (SNR) are key performance metrics for evaluating MPI, which are directly influenced by the gradient of the selection field (SF). Increasing the SF gradient can improve the spatial resolution of MPI, but will lead to a decrease in SNR. Deep learning (DL) methods may enable obtaining high-resolution images from low-resolution images to improve the MPI resolution under low gradient conditions. However, existing DL methods overlook the physical procedures contributing to the blurring of MPI images, resulting in low interpretability and hindering breakthroughs in resolution. To address this issue, we propose a dual-channel end-to-end network with prior knowledge embedding for MPI (DENPK-MPI) to effectively establish a latent mapping between low-gradient and high-gradient images, thus improving MPI resolution without compromising SNR. By seamlessly integrating MPI PSF with DL paradigm, DENPK-MPI leads to a significant improvement in spatial resolution performance. Simulation, phantom, and in vivo MPI experiments have collectively confirmed that our method can improve the resolution of low-gradient MPI images without sacrificing SNR, resulting in a decrease in full width at half maximum by 14.8%-23.8 %, and the accuracy of image reconstruction is 18.2 %-27.3 % higher than other DL methods. In conclusion, we propose a DL method that incorporates MPI prior knowledge, which can improve the spatial resolution of MPI without compromising SNR and possess improved biomedical application.

Targeted mitigation of neointimal hyperplasia via magnetic field-directed localization of superparamagnetic iron oxide nanoparticle-labeled endothelial progenitor cells following carotid balloon catheter injury in rats.

BACKGROUND: The transplantation of endothelial progenitor cells (EPCs) has been shown to reduce neointimal hyperplasia following arterial injury. However, the efficacy of this approach is hampered by limited homing of EPCs to the injury site. Additionally, the in vivo recruitment and metabolic activity of transplanted EPCs have not been continuously monitored. METHODS: EPCs were labeled with indocyanine green (ICG)-conjugated superparamagnetic iron oxide nanoparticles (SPIONs) and subjected to external magnetic field targeting to enhance their delivery to a carotid balloon injury (BI) model in Sprague-Dawley rats. Magnetic particle imaging (MPI)/ fluorescence imaging (FLI) multimodal in vivo imaging, 3D MPI/CT imaging and MPI/FLI ex vivo imaging was performed after injury. Carotid arteries were collected and analyzed for pathology and immunofluorescence staining. The paracrine effects were analyzed by enzyme-linked immunosorbent assay. RESULTS: The application of a magnetic field significantly enhanced the localization and retention of SPIONs@PEG-ICG-EPCs at the site of arterial injury, as evidenced by both in vivo continuous monitoring and ex vivo by observation. This targeted delivery approach effectively inhibited neointimal hyperplasia and increased the presence of CD31-positive cells at the injury site. Moreover, serum levels of SDF-1α, VEGF, IGF-1, and TGF-ß1 were significantly elevated, indicating enhanced paracrine activity. CONCLUSIONS: Our findings demonstrate that external magnetic field-directed delivery of SPIONs@PEG-ICG-EPCs to areas of arterial injury can significantly enhance their therapeutic efficacy. This enhancement is likely mediated through increased paracrine signaling. These results underscore the potential of magnetically guided SPIONs@PEG-ICG-EPCs delivery as a promising strategy for treating arterial injuries.

GPU-accelerated parallel image reconstruction strategies for magnetic particle imaging.

Objective. Image reconstruction is a fundamental step in magnetic particle imaging (MPI). One of the main challenges is the fact that the reconstructions are computationally intensive and time-consuming, so choosing an algorithm presents a compromise between accuracy and execution time, which depends on the application. This work proposes a method that provides both fast and accurate image reconstructions.Approach. Image reconstruction algorithms were implemented to be executed in parallel ingraphics processing units(GPUs) using the CUDA framework. The calculation of the model-based MPI calibration matrix was also implemented in GPU to allow both fast and flexible reconstructions.Main results. The parallel algorithms were able to accelerate the reconstructions by up to about6,100times in comparison to the serial Kaczmarz algorithm executed in the CPU, allowing for real-time applications. Reconstructions using the OpenMPIData dataset validated the proposed algorithms and demonstrated that they are able to provide both fast and accurate reconstructions. The calculation of the calibration matrix was accelerated by up to about 37 times.Significance. The parallel algorithms proposed in this work can provide single-frame MPI reconstructions in real time, with frame rates greater than 100 frames per second. The parallel calculation of the calibration matrix can be combined with the parallel reconstruction to deliver images in less time than the serial Kaczmarz reconstruction, potentially eliminating the need of storing the calibration matrix in the main memory, and providing the flexibility of redefining scanning and reconstruction parameters during execution.

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

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

A greedy regularized block Kaczmarz method for accelerating reconstruction in magnetic particle imaging.

Objective.Magnetic particle imaging (MPI) is an emerging medical tomographic imaging modality that enables real-time imaging with high sensitivity and high spatial and temporal resolution. For the system matrix reconstruction method, the MPI reconstruction problem is an ill-posed inverse problem that is commonly solved using the Kaczmarz algorithm. However, the high computation time of the Kaczmarz algorithm, which restricts MPI reconstruction speed, has limited the development of potential clinical applications for real-time MPI. In order to achieve fast reconstruction in real-time MPI, we propose a greedy regularized block Kaczmarz method (GRBK) which accelerates MPI reconstruction.Approach.GRBK is composed of a greedy partition strategy for the system matrix, which enables preprocessing of the system matrix into well-conditioned blocks to facilitate the convergence of the block Kaczmarz algorithm, and a regularized block Kaczmarz algorithm, which enables fast and accurate MPI image reconstruction at the same time.Main results.We quantitatively evaluated our GRBK using simulation data from three phantoms at 20 dB, 30 dB, and 40 dB noise levels. The results showed that GRBK can improve reconstruction speed by single orders of magnitude compared to the prevalent regularized Kaczmarz algorithm including Tikhonov regularization, the non-negative Fused Lasso, and wavelet-based sparse model. We also evaluated our method on OpenMPIData, which is real MPI data. The results showed that our GRBK is better suited for real-time MPI reconstruction than current state-of-the-art reconstruction algorithms in terms of reconstruction speed as well as image quality.Significance.Our proposed method is expected to be the preferred choice for potential applications of real-time MPI.

Active Targeting Hyaluronan Conjugated Nanoprobe for Magnetic Particle Imaging and Near-Infrared Fluorescence Imaging of Breast Cancer and Lung Metastasis.

A major contributing cause to breast cancer related death is metastasis. Moreover, breast cancer metastasis often shows little symptoms until a large area of the organs is occupied by metastatic cancer cells. Breast cancer multimodal imaging is attractive since it integrates advantages from several modalities, enabling more accurate cancer detection. Glycoprotein CD44 is overexpressed on most breast cancer cells and is the primary cell surface receptor for hyaluronan (HA). To facilitate breast cancer diagnosis, we report an indocyanine green (ICG) and HA conjugated iron oxide nanoparticle (NP-ICG-HA), which enabled active targeting to breast cancer by HA-CD44 interaction and detected metastasis with magnetic particle imaging (MPI) and near-infrared fluorescence imaging (NIR-FI). When evaluated in a transgenic breast cancer mouse model, NP-ICG-HA enabled the detection of multiple breast tumors in MPI and NIR-FI, providing more comprehensive images and a diagnosis of breast cancer. Furthermore, NP-ICG-HAs were evaluated in a lung metastasis model. Upon NP-ICG-HA administration, MPI showed clear signals in the lungs, indicating the tumor sites. This is the first time that HA-based NPs have enabled MPI of cancer. NP-ICG-HAs are an attractive platform for noninvasive detection of primary breast cancer and lung metastasis.

A systematic 3-D magnetic particle imaging simulation model for quantitative analysis of reconstruction image quality.

BACKGROUND AND OBJECTIVE: Magnetic particle imaging (MPI) is an emerging imaging technology in medical tomography that utilizes the nonlinear magnetization response of superparamagnetic iron oxide (SPIO) particles to determine the in vivo spatial distribution of nanoparticle contrast agents. The reconstruction image quality of MPI is determined by the characteristics of magnetic particles, the setting of the MPI scanner parameters, and the hardware interference of MPI systems. We explore a feasible method to systematically and quickly analyze the impact of these factors on MPI reconstruction image quality. METHODS: We propose a systematic 3-D MPI simulation model. The MPI simulation model has the capability of quickly producing the simulated reconstruction images of a scanned phantom, and quantitative analysis of MPI reconstruction image quality can be achieved by comparing the differences between the input image and output image. These factors are mainly classified as imaging parameters and interference parameters in our model. In order to reduce the computational time of the simulation model, we introduce GPU parallel programming to accelerate the processing of large complex matrix data. For ease of use, we also construct a reliable, high-performance, and open-source 3-D MPI simulation software tool based on our model. The efficiency of our model is evaluated by using OpenMPIData. To demonstrate the capabilities of our model, we conduct simulation experiments using parameters consistent with a real MPI scanner for improving MPI image quality. RESULTS: The experimental results show that our simulation model can systematically and quickly evaluate the impact of imaging parameters and interference parameters on MPI reconstruction image quality. CONCLUSIONS: We developed an easy-to-use and open-source 3-D MPI simulation software tool based on our simulation model incorporating all the stages of MPI formation, from signal acquisition to image reconstruction. In the future, our simulation model has potential guiding significance to practical MPI images.

A fused LASSO operator for fast 3D magnetic particle imaging reconstruction.

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.

MPIGAN: An end-to-end deep based generative framework for high-resolution magnetic particle imaging reconstruction.

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.

Recent Developments in Magnetic Hyperthermia Therapy (MHT) and Magnetic Particle Imaging (MPI) in the Brain Tumor Field: A Scoping Review and Meta-Analysis.

Magnetic hyperthermia therapy (MHT) is a promising treatment modality for brain tumors using magnetic nanoparticles (MNPs) locally delivered to the tumor and activated with an external alternating magnetic field (AMF) to generate antitumor effects through localized heating. Magnetic particle imaging (MPI) is an emerging technology offering strong signal-to-noise for nanoparticle localization. A scoping review was performed by systematically querying Pubmed, Scopus, and Embase. In total, 251 articles were returned, 12 included. Articles were analyzed for nanoparticle type used, MHT parameters, and MPI applications. Preliminary results show that MHT is an exciting treatment modality with unique advantages over current heat-based therapies for brain cancer. Effective application relies on the further development of unique magnetic nanoparticle constructs and imaging modalities, such as MPI, that can enable real-time MNP imaging for improved therapeutic outcomes.

Post-synthesis Oxidation of Superparamagnetic Iron Oxide Nanoparticles to Enhance Magnetic Particle Imaging Performance.

This study investigates the impact of post-synthesis oxidation on the performance of superparamagnetic iron oxide nanoparticles (SPIONs) in magnetic particle imaging (MPI), an emerging technology with applications in diagnostic imaging and theranostics. SPIONs synthesized from iron oleate were subjected to a post-synthesis oxidation treatment with a 1% Oxygen in Argon mixture. MPI performance, gauged via signal intensity and resolution using a MOMENTUM™ scanner, was correlated to the nanoparticles' physical and magnetic properties. Post-synthesis oxidation did not alter physical attributes like size and shape, but significantly enhanced magnetic properties. Saturation magnetization increased from 52% to 93% of the bulk value for magnetite, leading to better MPI performance in terms of signal intensity and resolution. However, the observed MPI performance did not fully align with predictions based on the ideal Langevin model, indicating the need for considering factors like relaxation and shape anisotropy. The findings underscore the potential of post-synthesis oxidation as a method to fine-tune magnetic properties of SPIONs and improve MPI performance, and the need for reproducible synthesis methods that afford finely tuned control of nanoparticle size, shape, and magnetic properties.

Development of high-efficiency superparamagnetic drug delivery system with MPI imaging capability.

In this study, a high-efficiency superparamagnetic drug delivery system was developed for preclinical treatment of bladder cancer in small animals. Two types of nanoparticles with magnetic particle imaging (MPI) capability, i.e., single- and multi-core superparamagnetic iron oxide nanoparticles (SPIONs), were selected and coupled with bladder anti-tumor drugs by a covalent coupling scheme. Owing to the minimal particle size, magnetic field strengths of 270 mT with a gradient of 3.2 T/m and 260 mT with a gradient of 3.7 T/m were found to be necessary to reach an average velocity of 2 mm/s for single- and multi-core SPIONs, respectively. To achieve this, a method of constructing an in vitro magnetic field for drug delivery was developed based on hollow multi-coils arranged coaxially in close rows, and magnetic field simulation was used to study the laws of the influence of the coil structure and parameters on the magnetic field. Using this method, a magnetic drug delivery system of single-core SPIONs was developed for rabbit bladder therapy. The delivery system consisted of three coaxially and equidistantly arranged coils with an inner diameter of Φ50 mm, radial height of 85 mm, and width of 15 mm that were positioned in close proximity to each other. CCK8 experimental results showed that the three types of drug-coupled SPION killed tumor cells effectively. By adjusting the axial and radial positions of the rabbit bladder within the inner hole of the delivery coil structure, the magnetic drugs injected could undergo two-dimensional delivery motions and were delivered and aggregated to the specified target location within 12 s, with an aggregation range of about 5 mm × 5 mm. In addition, the SPION distribution before and after delivery was imaged using a home-made open-bore MPI system that could realistically reflect the physical state. This study contributes to the development of local, rapid, and precise drug delivery and the visualization of this process during cancer therapy, and further research on MPI/delivery synchronization technology is planned for the future.

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

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

First magnetic particle imaging to assess pulmonary vascular leakage in vivo in the acutely injured and fibrotic lung.

Increased pulmonary vascular permeability is a characteristic feature of lung injury. However, there are no established methods that allow the three-dimensional visualization and quantification of pulmonary vascular permeability in vivo. Evans blue extravasation test and total protein test of bronchoalveolar lavage fluid (BALF) are permeability assays commonly used in research settings. However, they lack the ability to identify the spatial and temporal heterogeneity of endothelial barrier disruption, which is typical in lung injuries. Magnetic resonance (MR) and near-infrared (NIR) imaging have been proposed to image pulmonary permeability, but suffer from limited sensitivity and penetration depth, respectively. In this study, we report the first use of magnetic particle imaging (MPI) to assess pulmonary vascular leakage noninvasively in vivo in mice. A dextran-coated superparamagnetic iron oxide (SPIO), synomag®, was employed as the imaging tracer, and pulmonary SPIO extravasation was imaged and quantified to evaluate the vascular leakage. Animal models of acute lung injury and pulmonary fibrosis (PF) were used to validate the proposed method. MPI sensitively detected the SPIO extravasation in both acutely injured and fibrotic lungs in vivo, which was confirmed by ex vivo imaging and Prussian blue staining. Moreover, 3D MPI illustrated the spatial heterogeneity of vascular leakage, which correlated well with CT findings. Based on the in vivo 3D MPI images, we defined the SPIO extravasation index (SEI) to quantify the vascular leakage. A significant increase in SEI was observed in the injured lungs, in consistent with the results obtained via ex vivo permeability assays. Overall, our results demonstrate that 3D quantitative MPI serves as a useful tool to examine pulmonary vascular integrity in vivo, which shows promise for future clinical translation.