Magnetic Particle Imaging Signal Research Articles

Abstract 4135: First demonstration of magnetic particle imaging for sentinel lymph node identification

Abstract Sentinel Lymph Node (LN) biopsy involves the identification and surgical removal of the first LN(s) that drain from a primary tumor to evaluate for metastasis by histopathology [1]. For several tumor types, the standard of care is to manage the regional LN basin separately from the primary tumor. For head and neck cancer, melanoma, and complex breast cancer cases, pre-surgical imaging is required to determine the number and location of LN(s) to remove. Most sentinel LN biopsies are performed with nuclear imaging, which relies on short-lived radiotracers and can have poor image quality, making it challenging to identify sentinel LNs in complex anatomies. An alternative and non-inferior workflow uses a non-radioactive iron oxide magnetic tracer (ferucarbotran) with a magnetic probe [2], however this tool can only be used intraoperatively. In this abstract we introduce magnetic particle imaging (MPI) as a pre-surgical imaging technology that detects iron oxides with high sensitivity at mm-scale resolution [3]. Our objective is to demonstrate that MPI provides sensitive and quantitative tracking of ferucarbotran pharmacokinetics from four anatomical sites to primary draining LNs in mice. Methods: Ferucarbotran was administered intradermally to C57BL/6 mice at a standard clinical dose of 0.675 mg Fe/kg to the forepaw, hindpaw, or base of tail, or tongue (n = 4). Full-body 2D and 3D imaging was performed after 20 mins, 24 h, and 48 h, and 144 h using MOMENTUM imager (Magnetic Insight Inc.). MPI signal was quantified at the injection site and draining LNs. LNs of interest were extracted to verify MPI signals ex vivo then were processed for Perl’s Prussian iron staining. Results: After 20 minutes, MPI signal was seen at the injection site and primary LNs. The pharmacokinetics of ferucarbotran to LNs varied based on administration site. For hindpaw, signal was present in the popliteal LN (1.4% of tracer). For forepaw, ferucarbotran accumulated in the primary axillary LN (9%). For base of the tail, signal was observed in inguinal LN (2%). For tongue, MPI signal was detected in cervical LNs (14%). In all mice, MPI signal at the injection site decreased over time and signal in primary LNs persisted for at least 6 days. Conclusions: We demonstrated MPI is a quantitative, hotspot imaging technique for identifying primary LNs. As iron oxide tracer pharmacokinetics varies with injection site, this imaging technique could provide fundamental information required for surgical planning. Unlike nuclear imaging, persistence of MPI signal for several days provides tremendous flexibility in clinical workflow and introduces the potential for an image-guide delayed SLN biopsy [4] . This preclinical LN imaging is timely as our team is actively building and testing the world’s first large-bore, clinical-scale MPI scanner.[1] Leong SP, Clin Exp Metastasis (2022). [2] Alvarado MD, et al. Ann Surg Oncol (2019). [3] Sarnitas EU, et al. J Magn Reson (2014). [4] Karakatsanis A, et al. Ann Surg Oncol (2023). Citation Format: Olivia C. Sehl, A. Rahman Mohtasebzadeh, Kelvin Guo, Petrina Kim, Benjamin Fellows, Marcela Weyhmiller, Paula J. Foster, Patrick W. Goodwill, Joan M. Greve. First demonstration of magnetic particle imaging for sentinel lymph node identification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4135.

Bare-Metal Stent Tracking with Magnetic Particle Imaging.

Magnetic particle imaging (MPI) is an emerging medical imaging modality that is on the verge of clinical use. In recent years, cardiovascular applications have shown huge potential like, e.g., intraprocedural imaging guidance of stent placement through MPI. Due to the lack of signal generation, nano-modifications have been necessary to visualize commercial medical instruments until now. In this work, it is investigated if commercial interventional devices can be tracked with MPI without any nano-modification. Potential MPI signal generation of nine endovascular metal stents was tested in a commercial MPI scanner. Two of the stents revealed sufficient MPI signal. Because one of the two stents showed relevant heating, the imaging experiments were carried out with a single stent model (Boston Scientific/Wallstent-Uni Endoprothesis, diameter: 16 mm, length: 60 mm). The nitinol stent and its delivery system were investigated in seven different scenarios. Therefore, the samples were placed at 49 defined spatial positions by a robot in a meandering pattern during MPI scans. Image reconstruction was performed, and the mean absolute errors (MAE) between the signals' centers of mass (COM) and ground truth positions were calculated. The stent material was investigated by magnetic particle spectroscopy (MPS) and vibrating sample magnetometry (VSM). To detect metallic components within the delivery system, nondestructive testing via computed tomography was performed. The tracking of the stent and its delivery system was possible without any nano-modification. The MAE of the COM were 1.49 mm for the stent mounted on the delivery system, 3.70 mm for the expanded stent and 1.46 mm for the delivery system without the stent. The results of the MPS and VSM measurements indicate that besides material properties eddy currents seem to be responsible for signal generation. It is possible to image medical instruments with dedicated designs without modifications by means of MPI. This enables a variety of applications without compromising the mechanical and biocompatible properties of the instruments.

Machine Learning and Deep Learning Applications in Magnetic Particle Imaging.

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. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.

Space-Specific Mixing Excitation for High-SNR Spatial Encoding in Magnetic Particle Imaging.

Magnetic Particle Imaging (MPI) is a radiation-free tracer-based imaging technology that visualizes the spatial distribution of superparamagnetic iron oxide nanoparticles. Conventional spatial encoding methods in MPI rely on a gradient magnetic field with a constant gradient strength to generate a field-free point or line for spatial scanning. However, increasing the gradient strength can enhance theoretical spatial resolution but also lead to a decrease in the Signal-to-Noise Ratio (SNR) and sensitivity of the imaging system. This poses a technical challenge in balancing spatial resolution and sensitivity, necessitating intricate hardware design. To address this, we present a Space-Specific Mixing Excitation (SSME) technique for achieving high-SNR spatial encoding in MPI. By utilizing a dual-frequency excitation magnetic field with a non-homogeneous field strength, magnetic particles at each position generate unique intermodulation responses. By performing multi-channel acquisitions across the entire field of view, high SNR MPI signals can be acquired. When combined with reconstruction techniques based on system matrix, multi-dimensional SSME-MPI can be achieved. The effectiveness of the proposed method was validated through phantom and in vivo imaging experiments. The results demonstrate significant improvements in sensitivity (3.6-fold improvement) and spatial resolution (better than 1 mm) without any hardware modifications. These findings demonstrate the capability of SSME to enhance both the spatial resolution and sensitivity of MPI. This method provides a solution to the ongoing challenge of balancing spatial resolution and sensitivity in MPI, potentially facilitating the implementation of MPI in a wider range of medical applications.

Quantitative visualization of myocardial ischemia-reperfusion-induced cardiac lesions via ferroptosis magnetic particle imaging.

Myocardial ischemia-reperfusion (MI/R) injury is a complication in vascular reperfusion therapy for MI, occurring in approximately 60% of patients. Ferroptosis is an important process in the development of MI/R cardiac lesions. Transferrin receptor 1 (TfR1), a marker of ferroptosis, corresponds to the changes in MI/R cardiac lesions and is expected to be a biomarker for detecting MI/R-induced ferroptosis. However, the noninvasive in vivo visualization of ferroptosis in MI/R is a big challenge. Thus, this study aimed to develop a novel multimodal imaging platform to identify markers of MI/R cardiac lesions in vivo through targeting TfR1. Methods: Magnetic particle imaging (MPI) modality for ferroptosis based on superparamagnetic cubic-iron oxide nanoparticles (SCIO NPs), named feMPI, has been developed. FeMPI used TfR1 as a typical biomarker. The feMPI probe (SCIO-ICG-CRT-CPPs NPs, CCI NPs) consists of SCIO NPs, TfR1-targeting peptides (CRT), cell-penetrating peptides (CPPs), and indocyanine green (ICG). The specificity and sensitivity of CCI NPs in the MI/R mouse model were evaluated by MPI, magnetic resonance imaging (MRI), and near-infrared (NIR) fluorescent imaging. Results: The intensity of the MPI signal correlates linearly with the percentage of infarct area in MI/R stained by TTC, enabling a quantitative assessment of the extent of cardiac lesions. Notably, these findings are consistent with the standard clinical biochemical indicators in MI/R within the first 24 h. FeMPI detects cardiac injury approximately 48 h prior to the current clinical imaging detection methods of MI/R. Conclusion: The feMPI strategy can be a powerful tool for studying the process of MI/R-induced ferroptosis in vivo, providing clues for molecular imaging and drug development of ferroptosis-related treatments.

Adhesion molecule-targeted magnetic particle imaging nanoprobe for visualization of inflammation in acute lung injury.

Uncontrolled intra-alveolar inflammation is a central pathogenic feature, and its severity translates into a valid prognostic indicator of acute lung injury (ALI). Unfortunately, current clinical imaging approaches are unsuitable for visualizing and quantifying intra-alveolar inflammation. This study aimed to construct a small-sized vascular cell adhesion molecule-1 (VCAM-1)-targeted magnetic particle imaging (MPI) nanoprobe (ESPVPN) to visualize and accurately quantify intra-alveolar inflammation at the molecular level. ESPVPN was engineered by conjugating a peptide (VHPKQHRGGSK(Cy7)GC) onto a polydopamine-functionalized superparamagnetic iron oxide core. The MPI performance, targeting, and biosafety of the ESPVPN were characterized. VCAM-1 expression in HUVECs and mouse models was evaluated by western blot. The degree of inflammation and distribution of VCAM-1 in the lungs were assessed using histopathology. The expression of pro-inflammatory markers and VCAM-1 in lung tissue lysates was measured using ELISA. After intravenous administration of ESPVPN, MPI and CT imaging were used to analyze the distribution of ESPVPN in the lungs of the LPS-induced ALI models. The small-sized (~10 nm) ESPVPN exhibited superior MPI performance compared to commercial MagImaging® and Vivotrax, and ESPVPN had effective targeting and biosafety. VCAM-1 was highly expressed in LPS-induced ALI mice. VCAM-1 expression was positively correlated with the LPS-induced dose (R = 0.9381). The in vivo MPI signal showed positive correlations with both VCAM-1 expression (R = 0.9186) and representative pro-inflammatory markers (MPO, TNF-α, IL-6, IL-8, and IL-1β, R > 0.7). ESPVPN effectively targeted inflammatory lungs and combined the advantages of MPI quantitative imaging to visualize and evaluate the degree of ALI inflammation.

Small zinc doped iron oxide tracers for magnetic particle imaging

Magnetic particle imaging (MPI) has garnered significant attention in biomedical imaging research due to its excellent signal intensity that is generated directly from superparamagnetic iron oxide nanoparticles (SPIONs). Small nanoparticle tracers with high saturation magnetisation are crucial for MPI as they can prolong circulation, crossing the blood brain barrier and enhance cellular uptake. In this work, we demonstrate small zinc doped iron oxide nanoparticles (Zn-IONPs) are excellent MPI tracers. Our Zn-IONPs exhibited up to 37 % and 64% enhancement in saturation magnetisation (Msat) value and MPI signal intensity respectively compared to Fe3O4 of the same size. As a result, the polymer encapsulated Zn-IONPs achieved up to 2.7-fold enhancement in MPI signal intensity compared to VivoTrax. Furthermore, these polymer encapsulated NPs were also determined to be non-toxic hence making these Zn-IONPs ideal for many biomedical applications in MPI where small size is critical to prolong circulation time and crossing the blood brain barrier.

In vivo tracking of adenoviral-transduced iron oxide-labeled bone marrow-derived dendritic cells using magnetic particle imaging

BackgroundDespite widespread study of dendritic cell (DC)-based cancer immunotherapies, the in vivo postinjection fate of DC remains largely unknown. Due in part to a lack of quantifiable imaging modalities, this is troubling as the amount of DC migration to secondary lymphoid organs correlates with therapeutic efficacy. Magnetic particle imaging (MPI) has emerged as a suitable modality to quantify in vivo migration of superparamagnetic iron oxide (SPIO)-labeled DC. Herein, we describe a popliteal lymph node (pLN)-focused MPI scan to quantify DC in vivo migration accurately and consistently.MethodsAdenovirus (Ad)-transduced SPIO+ (Ad SPIO+) and SPIO+ C57BL/6 bone marrow-derived DC were generated and assessed for viability and phenotype, then fluorescently labeled and injected into mouse hind footpads (n = 6). Two days later, in vivo DC migration was quantified using whole animal, pLN-focused, and ex vivo pLN MPI scans.ResultsNo significant differences in viability, phenotype and in vivo pLN migration were noted for Ad SPIO+ and SPIO+ DC. Day 2 pLN-focused MPI quantified DC migration in all instances while whole animal MPI only quantified pLN migration in 75% of cases. Ex vivo MPI and fluorescence microscopy confirmed that pLN MPI signal was due to originally injected Ad SPIO+ and SPIO+ DC.ConclusionWe overcame a reported limitation of MPI by using a pLN-focused MPI scan to quantify pLN-migrated Ad SPIO+ and SPIO+ DC in 100% of cases and detected as few as 1000 DC (4.4 ng Fe) in vivo. MPI is a suitable preclinical imaging modality to assess DC-based cancer immunotherapeutic efficacy.Relevance statementTracking the in vivo fate of DC using noninvasive quantifiable magnetic particle imaging can potentially serve as a surrogate marker of therapeutic effectiveness.Key points• Adenoviral-transduced and iron oxide-labeled dendritic cells are in vivo migration competent.• Magnetic particle imaging is a suitable modality to quantify in vivo dendritic cell migration.• Magnetic particle imaging focused field of view overcomes dynamic range limitation.Graphical

Self-supervised Signal Denoising for Magnetic Particle Imaging.

Magnetic particle imaging (MPI) is a medical imaging technology with high resolution and high sensitivity, which tracks the distribution of superparamagnetic iron oxide nanoparticles (SPIONs) in the nonlinear response to dynamic excitation at a field-free region. However, various noises distort the signals resulting in a decline in imaging quality. Traditional threshold-based methods cannot remove dynamic noise in MPI signals. Therefore, a self-supervised denoising method is proposed to denoise MPI signals in this study. The approach adopted U-net as the backbone and modified the network for MPI signals. The network is trained using two periods of noisy signals and the shape prior knowledge of the MPI signals is introduced for promoting the convergence of the self-supervised net. The experiments show that the learning-based method can still denoising the MPI signal without labeling data and eventually improve image quality, and our approach can achieve the best performance compared with other self-supervised methods in MPI signal denoising.

Deep Penetrating and Sensitive Targeted Magnetic Particle Imaging and Photothermal Therapy of Early-Stage Glioblastoma Based on a Biomimetic Nanoplatform.

Early diagnosis can effectively improve the survival of glioblastoma multiforme (GBM). A specific imaging technique that is simultaneously deep penetrating and sensitive to small tissue changes is desired to identify GBM. Due to its excellent features in signal contrast, detection sensitivity, and none or little attenuation in tissue, magnetic particle imaging (MPI) possesses great potential in cancer diagnosis, especially when the imaging modality is equipped with specifically targeted nanoprobes. However, when gliomas are small, the blood-brain barrier (BBB) is complete and prevents nanoprobes from entering the brain, which negates the theranostic effect. This study proposes a biomimetic nanoplatform that assist the MPI tracers in breaking through the BBB and then demonstrate a targeted and sensitive diagnosis of GBM. Afterward, the photothermal therapy and immune regulationshow an excellent therapeutic effect on the GBM. It is experimentally confirmed that the MPI signal does not decay with tissue depthand shows excellent sensitivity for thousands-cells. Only small animals are conducted in this study due to the limitations of the current commercial MPI scanner, however, this research theoretically enables large animal and human studies, which encourages a promising pathway toward the noninvasive diagnosis of early-stage GBM in clinics.

Sensitive magnetic particle imaging of haemoglobin degradation for the detection and monitoring of intraplaque haemorrhage in atherosclerosis.

Intraplaque haemorrhage (IPH) drives atherosclerosis progression and is a key imaging biomarker of unstable plaques. Non-invasive and sensitive monitoring of IPH is challenging due to the compositional complexity and dynamic nature of atherosclerotic plaques. Magnetic particle imaging (MPI) is a highly sensitive, radiation-free, and no-tissue-background tomographic technique that detects superparamagnetic nanoparticles. Thus, we aimed to investigate whether MPI can invivo detect and monitor IPH. Thirty human carotid endarterectomy samples were collected and scanned with MPI. The tandem stenosis (TS) model was employed to establish unstable plaques with IPH in ApoE-/- mice. MPI and 7TT1-weighted magnetic resonance imaging (MRI) were performed on TS ApoE-/- mice. Plaque specimens were analyzed histologically. Human carotid endarterectomy samples exhibited endogenous MPI signals, which histologically colocalized with IPH. Invitro experiments identified haemosiderin, a haemoglobin degradation product, as a potential source of MPI signals. Longitudinal MPI of TS ApoE-/- mice detected IPH at unstable plaques, of which MPI signal-to-noise ratio values increased from 6.43±1.74 (four weeks) to 10.55±2.30 (seven weeks) and reduced to 7.23±1.44 (eleven weeks). In contrast, 7TT1-weighted MRI did not detect the small-size IPH (329.91±226.82μm2) at four weeks post-TS. The time-course changes in IPH were shown to correlate with neovessel permeability providing a possible mechanism for signal changes over time. MPI is a highly sensitive imaging technology that allows the identification of atherosclerotic plaques with IPH and may help detect and monitor unstable plaques in patients. This work was supported in part by the Beijing Natural Science Foundation under Grant JQ22023; the National Key Research and Development Program of China under Grant 2017YFA0700401; the National Natural Science Foundation of China under Grant 62027901, 81827808, 81730050, 81870178, 81800221, 81527805, and 81671851; the CAS Youth Innovation Promotion Association under Grant Y2022055 and CAS Key Technology Talent Program; and the Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai HLHPTP201703).

Application of magnetic particle imaging to evaluate nanoparticle fate in rodent joints.

Nanoparticles are a promising approach for improving intra-articular drug delivery and tissue targeting. However, techniques to non-invasively track and quantify their concentration in vivo are limited, resulting in an inadequate understanding of their retention, clearance, and biodistribution in the joint. Currently, fluorescence imaging is often used to track nanoparticle fate in animal models; however, this approach has limitations that impede long-term quantitative assessment of nanoparticles over time. The goal of this work was to evaluate an emerging imaging modality, magnetic particle imaging (MPI), for intra-articular tracking of nanoparticles. MPI provides 3D visualization and depth-independent quantification of superparamagnetic iron oxide nanoparticle (SPION) tracers. Here, we developed and characterized a polymer-based magnetic nanoparticle system incorporated with SPION tracers and cartilage targeting properties. MPI was then used to longitudinally assess nanoparticle fate after intra-articular injection. Magnetic nanoparticles were injected into the joints of healthy mice, and evaluated for nanoparticle retention, biodistribution, and clearance over 6weeks using MPI. In parallel, the fate of fluorescently tagged nanoparticles was tracked using in vivo fluorescence imaging. The study was concluded at day 42, and MPI and fluorescence imaging demonstrated different profiles in nanoparticle retention and clearance from the joint. MPI signal was persistent over the study duration, suggesting NP retention of at least 42days, much longer than the 14days observed based on fluorescence signal. These data suggest that the type of tracer - SPIONs or fluorophores - and modality of imaging can affect interpretation of nanoparticle fate in the joint. Given that understanding particle fate over time is paramount for attaining insights about therapeutic profiles in vivo, our data suggest MPI may yield a quantitative and robust method to non-invasively track nanoparticles following intra-articular injection on an extended timeline.

Improved Quantitative Analysis Method for Magnetic Particle Imaging Based on Deblurring and Region Scalable Fitting.

Magnetic particle imaging (MPI) is a technique for imaging magnetic particle concentration distribution. It has the advantages of high sensitivity, no signal attenuation with depth, and no ionizing radiation. Although MPI has been widely used in the biomedical field, accurate image analysis has been challenging due to its anisotropic point spread function (PSF). The purpose of this study is to propose an MPI image restoring and segmentation method to facilitate a more precise quantitative evaluation of the magnetic particle imaging in vivo. We proposed a DeRSF method that combined deblurring and region scalable fitting (RSF)to determine the imaging tracer distribution. Then a uniform erosion and scaling criterion was established based on simulation experiments to correct the segmentation results, which was further validated on phantom imaging. Finally, we imaged the MPI tracer at gradient concentrations to establish the calibration curve between the MPI signal and iron mass for iron quantification in phantom and in vivo imaging. The phantom imaging experiments showed that our method achieved improved segmentation performance. The mean value of the dice coefficients for segmentation was up to 0.86, demonstrating that our method can accurately map and quantify the distribution of the tracer. Moreover, the iron quantification on both phantom and in vivo mouse imaging was realized with the minimal error of 5.50%, by our established calibration curve. Our proposed DeRSF method was successfully used for improved MPI quantitative analysis. More importantly, this method also showed accurate quantitative results on images with different shapes and tracer concentrations in both phantom and in vivo data, which laid the foundation for the biomedical study of MPI.

Multi-scale dual domain network for nonlinear magnetization signal filtering in magnetic particle imaging

Magnetic particle imaging (MPI) realizes functional imaging by generating nonlinear response signals in the magnetic field through magnetic nanoparticles. MPI is a highly sensitive tracer-based imaging technology, which opens new possibilities for promising biomedical applications. However, in practice, the background signals, including random thermal noise from MPI receive chains and the harmonic interference caused by nonlinear components under a high-frequency excitation field, restrict the MPI performance. In this study, we propose a learning-based method by training a multi-scale dual-domain network to effectively filter the MPI signals. In the model, a multi-channel filtering module was designed to suppress the noise-related features in time and frequency domains. We constructed four different MPI signal datasets including simulated and measured noises acquired from a homemade MPI system to verify our method. The signal filtering tests were performed on synthetic and measured data. The experimental results indicated that our method can achieve the best performance among state-of-art signal filtering methods. Especially, on the dataset containing measured noise, our proposal improved the signal-to-noise ratio from 6.88 dB to 29.11 dB. Moreover, the percentage root means square difference was reduced from 51.26 to 3.96 and the root mean square error was reduced from 65.07×10-4 to 5.29×10-4.

Dual Magnetic Particle Imaging and Akaluc Bioluminescence Imaging for Tracking Cancer Cell Metastasis.

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.

Progress in magnetic particle imaging signal and iron quantification methods in vivo - application to long circulating SPIONs.

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.

On the Receive Path Calibration of Magnetic Particle Imaging Systems

Magnetic nanoparticles are a valuable tool in many biomedical applications and can be used for diagnostic and therapeutic purposes. In magnetic particle imaging (MPI) and magnetic particle spectroscopy (MPS), the particles are subjected to a dynamic magnetic field and the particle magnetization response is simultaneously measured using one or multiple receive coils. Separating the particle signal from the feed-through signal is commonly done by advanced passive filtering, which distorts the particle signal. To correct this distortion, the transfer function of the receive chain needs to be known. While in principle, the transfer function can be simulated, due to imperfections in the electronic components, it is often more accurate to determine the transfer function in a calibration procedure. Although this system calibration, utilizing a calibration-coil setup, has been done by several research groups in the past, a general description of the underlying calibration model and methodology is still missing. In this article, we provide a general multi-channel calibration procedure for inductive receive paths in MPI and a blueprint to investigate model and method uncertainties. We generalized the calibration procedure to also cover nonorthogonal and nonhomogeneous receive coils. Finally, we showcase the calibration procedure and uncertainty analysis on our custom MPS system and use the MPI transfer functions of misaligned receive coils to decouple their superimposed receive signals from the receive path. The findings enable the comparison of MPI signals from different devices and can be used to normalize measurements and system functions in devices with exchangeable receive coils.

Rapid TAURUS for Relaxation-Based Color Magnetic Particle Imaging.

Magnetic particle imaging (MPI) is a rapidly developing medical imaging modality that exploits the non-linear response of magnetic nanoparticles (MNPs). Color MPI widens the functionality of MPI, empowering it with the capability to distinguish different MNPs and/or MNP environments. The system function approach for color MPI relies on extensive calibrations that capture the differences in the harmonic responses of the MNPs. An alternative calibration-free x-space-based method called TAURUS estimates a map of the relaxation time constant, τ , by recovering the underlying mirror symmetry in the MPI signal. However, TAURUS requires a back and forth scanning of a given region, restricting its usage to slow trajectories with constant or piecewise constant focus fields (FFs). In this work, we propose a novel technique to increase the performance of TAURUS and enable τ map estimation for rapid and multi-dimensional trajectories. The proposed technique is based on correcting the distortions on mirror symmetry induced by time-varying FFs. We demonstrate via simulations and experiments in our in-house MPI scanner that the proposed method successfully estimates high-fidelity τ maps for rapid trajectories that provide orders of magnitude reduction in scanning time (over 300 fold for simulations and over 8 fold for experiments) while preserving the calibration-free property of TAURUS.

Magnetic Particle Imaging of Magnetotactic Bacteria as Living Contrast Agents Is Improved by Altering Magnetosome Arrangement.

Superparamagnetic iron oxide nanoparticles (SPIONs) can be used as imaging agents to differentiate between normal and diseased tissue or track cell movement. Magnetic particle imaging (MPI) detects the magnetic properties of SPIONs, providing quantitative and sensitive image data. MPI performance depends on the size, structure, and composition of nanoparticles. Magnetotactic bacteria produce magnetosomes with properties similar to those of synthetic nanoparticles, and these can be modified by mutating biosynthetic genes. The use of Magnetospirillum gryphiswaldense, MSR-1 with a mamJ deletion, containing clustered magnetosomes instead of typical linear chains, resulted in improved MPI signal and resolution. Bioluminescent MSR-1 with the mamJ deletion were administered into tumor-bearing and healthy mice. In vivo bioluminescence imaging revealed the viability of MSR-1, and MPI detected signals in livers and tumors. The development of living contrast agents offers opportunities for imaging and therapy with multimodality imaging guiding development of these agents by tracking the location, viability, and resulting biological effects.

Magnetic Particle Imaging Is a Sensitive In Vivo Imaging Modality for the Detection of Dendritic Cell Migration.

The purpose of this study was to evaluate magnetic particle imaging (MPI) as a method for the in vivo tracking of dendritic cells (DC). DC are used in cancer immunotherapy and must migrate from the site of implantation to lymph nodes to be effective. The magnitude of the ensuing T cell response is proportional to the number of lymph node-migrated DC. With current protocols, less than 10% of DC are expected to reach target nodes. Therefore, imaging techniques for studying DC migration must be sensitive and quantitative. Here, we describe the first study using MPI to detect and track DC injected into the footpads of C57BL/6 mice migrating to the popliteal lymph nodes (pLNs). DC were labelled with Synomag-D™ and injected into each hind footpad of C57BL/6 mice (n = 6). In vivoMPI was conducted immediately and repeated 48h later. The MPI signal was measured from images and related to the signal from a known number of cells to calculate iron content. DC numbers were estimated by dividing iron content in the image by the iron per cell measured from a separate cell sample. The presence of SPIO-labeled DC in nodes was validated by ex vivo MPI, histology, and fluorescence microscopy. Day 2 imaging showed a decrease in MPI signal in the footpads and an increase in signal at the pLNs, indicating DC migration. MPI signal was detected in the left pLN in four of the six miceand two of the six mice showed MPI signal in the right pLN. Ex vivo imaging detected signal in 11/12 nodes. We report a sensitivity of approximately 4000 cells (0.015µg Fe) in vivo and 2000 cells (0.007µg Fe) ex vivo. Here, we describe the first study to use MPI to detect and track DC in a migration model with immunotherapeutic applications. We also bring attention to the issue of resolving unequal signals within close proximity, a challenge for any pre-clinical study using a highly concentrated tracer bolus that shadows nearby lower signals.