Tomographic Field Free Line Magnetic Particle Imaging With an Open-Sided Scanner Configuration

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.

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.

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.

Magnetic particle image scanner based on asymmetric core-filled electromagnetic actuator

Monitoring the distribution of magnetic nanoparticles (MNPs) in the vascular system is an important task for the advancement of precision therapeutics and drug delivery. Despite active targeting using active motilities, it is required to visualize the position and concentration of carriers that reach the target, to promote the development of this technology. In this work, a feasibility study is presented on a tomographic scanner that allows monitoring of the injected carriers quantitatively in a relatively short interval. The device is based on a small-animal-scale asymmetric magnetic platform integrated with magnetic particle imaging technology. An optimized isotropic field-free region (FFR) generation method using a magnetic manipulation system (MMS) is derived and numerically investigated. The in-vitro and in-vivo tracking performances are demonstrated with a high position accuracy of approximately 1 mm. A newly proposed tracking method was developed, specialized in vascular system, with quick scanning time (about 1s). In this paper, the primary function of the proposed system is to track magnetic particles using a magnetic manipulation system. Through this, proposed method enables the conventional magnetic actuation systems to upgrade the functionalities of both manipulation and localization of magnetic objects.

iMPI: portable human-sized magnetic particle imaging scanner for real-time endovascular interventions

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.

Open structure magnetic particle imaging by nonlinear back projection tomography reconstruction

OBJECTIVES

In open structure MPI systems, the nonlinear variation of the field free lines in the large region of interest scanning process distorts the x-space image reconstruction. In this study, we propose a nonlinear field free line projection reconstruction algorithm to solve the edge distortion problem of open structure MPI imaging.

METHODS

First, we calculate the curvature change law of the field free line in the scanning process. Then, we design a nonlinear back projection reconstruction algorithm according to the nonlinear characteristics of the field free line in the scanning process. Finally, the nonlinear back projection reconstruction algorithm is used to complete the tomography of blood vessels.

RESULTS

The numerical calculation and simulation results show that the open structure MPI combined with a nonlinear back projection reconstruction algorithm can accomplish vascular fault reconstruction. The reconstruction algorithm proposed in this paper suppresses the edge distortion of the image and improves the positioning accuracy of the image. The size of the region of interest where distortions are low is increased 16 times by allowing 10.9% degradation in the gradient.

CONCLUSIONS

We provide a non-linear inverse projection reconstruction algorithm to reduce the structural artefacts caused by FFL distortion. It provides a reconstruction scheme for a large region of interest fine imaging of open structure FFL-MPI.

Implementation of the surface gradiometer receive coils for the improved detection limit and sensitivity in the single-sided MPI scanner

Objective.Magnetic Particle Imaging (MPI) promises to enhance diagnostic capabilities of the existing clinical imaging modalities. Traditional MPI scanners utilize cylindrical bore geometry that prevents scaling up the MPI to accommodate full human subject. Single-sided geometry, on the other hand, has all the hardware located on one side providing an unrestricted imaging volume.Approach.Our single-sided MPI device utilizes a field-free line topology with a single drive coil and a surface receive coil, which is used to detect the nanoparticles. Unlike closed bore systems, single-sided devices cannot adapt well established solenoid gradiometer receive coil, which result in impinging potential sensitivity gain.Main results.In this work we study multiple receive coil configurations with compensation for the purpose of removing feedthrough, whilst preserving the superparamagnetic iron oxide nanoparticle signal. Moreover, we present a compensated surface receive coil design that provides highest sensitivity in the single-sided geometry and demonstrate a new detection limit in a single-sided scanner of 100 ng of iron. In addition, we demonstrate 1D imaging of a sample without use of receive filter recovering signal at fundamental harmonic.Significance.These advancements in the receive chain are crucial for developing a practical MPI scanner with a single-sided geometry.

Gradient-Based Pulsed Excitation and Relaxation Encoding in Magnetic Particle Imaging

Magnetic particle imaging (MPI) is a radiation-free vessel- and target-imaging modality that can sensitively detect nanoparticles. A static magnetic gradient field, referred to as a selection field, is required in MPI to provide a field-free region (FFR) for spatial encoding. The image resolution of MPI is closely related to the size of the FFR, which is determined by the selection field gradient amplitude. Because of the limitations of existing gradient coil hardware, the image resolution of MPI cannot satisfy the clinical requirements of human in vivo imaging. Pulsed excitation has been confirmed to improve the image resolution of MPI by breaking down the 'relaxation wall.' This work proposes the use of a pulsed waveform magnetic gradient from magnetic resonance imaging to further improve the image resolution of MPI. Through alignment of the gradient direction along the field-free line (FFL), each location on the FFL is able to have a unique excitation field strength that generates a specific relaxation-induced decay signal. Through excitation of nanoparticles on the FFL with many gradient profiles, a high-resolution, one-dimensional (1D) image can be reconstructed on the FFL. For larger magnetic nanoparticles, simulation results revealed that a pulsed excitation field with a greater flat portion generates a 1D bar pattern phantom image with a higher correlation and spatial resolution. With parallel FFL and gradient coil movements, high-resolution, two-dimensional (2D) Shepp-Logan phantom and brain vessel maps were reconstructed through repetition of the spatially resolved measurement of magnetic nanoparticles on the FFL.

TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging

Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles (MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a calibration scan to measure the system matrix (SM), which is then used to set up an inverse problem to reconstruct images of the MNP distribution during subsequent scans. This calibration enables the reconstruction to sensitively account for various system imperfections. Yet time-consuming SM measurements have to be repeated under notable changes in system properties. Here, we introduce a novel deep learning approach for accelerated MPI calibration based on Transformers for SM super-resolution (TranSMS). Low-resolution SM measurements are performed using large MNP samples for improved signal-to-noise ratio efficiency, and the high-resolution SM is super-resolved via model-based deep learning. TranSMS leverages a vision transformer module to capture contextual relationships in low-resolution input images, a dense convolutional module for localizing high-resolution image features, and a data-consistency module to ensure measurement fidelity. Demonstrations on simulated and experimental data indicate that TranSMS significantly improves SM recovery and MPI reconstruction for up to 64-fold acceleration in two-dimensional imaging.

Recent developments of the reconstruction in magnetic particle imaging

Magnetic particle imaging (MPI) is an emerging molecular imaging technique with high sensitivity and temporal-spatial resolution. Image reconstruction is an important research topic in MPI, which converts an induced voltage signal into the image of superparamagnetic iron oxide particles concentration distribution. MPI reconstruction primarily involves system matrix- and x-space-based methods. In this review, we provide a detailed overview of the research status and future research trends of these two methods. In addition, we review the application of deep learning methods in MPI reconstruction and the current open sources of MPI. Finally, research opinions on MPI reconstruction are presented. We hope this review promotes the use of MPI in clinical applications.

1D imaging of a superparamagnetic iron oxide nanoparticle distribution by a single-sided FFL magnetic particle imaging scanner

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 instead of a field-free point, 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 one dimensional imaging of multiple rods phantoms with a single-sided field-free line MPI scanner. The results demonstrate that our scanner is capable of one dimensional imaging of phantoms with a spatial resolution of at least 7 mm without image processing.

Applications of Magnetic Particle Imaging in Biomedicine: Advancements and Prospects

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.

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

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

A Simulation Study for Three Dimensional Tomographic Field Free Line Magnetic Particle Imaging

Magnetic Particle Imaging (MPI) is an emerging modality that images the magnetic nanoparticle distribution inside the body. The method is based on the non-linear response of the magnetic nanoparticles to an applied magnetic field. In this study, we present simulation results for three-dimensional (3D) tomographic imaging using an open-bore MPI system that can electronically scan a field free line (FFL). A field of view with 26×26×10 mm³ volume is imaged with a relatively low gradient field of 0.5 T/m. Imaging results for two 3D phantoms are presented: a letter phantom and a vessel phantom with stenosis regions. Using the system-matrix based reconstruction approach, the images were obtained with the Algebraic reconstruction technique (ART) and alternating direction method of multipliers (ADMM) methods. The stenosis regions were visually recognizable in high SNR conditions with ADMM. The effect of low gradient strength became prominent with increasing noise level, resulting in interlayer coupling artifacts.Clinical relevance- Magnetic Particle Imaging (MPI) is a new tracer-based imaging modality with high-spatiotemporal resolution. MPI can map quantitative distribution of super-paramagnetic iron oxide nanoparticles introduced inside the body. A field free line scanning MPI system with an open configuration can enable imaging of human-size volumes for interventional operations. In this study, we present simulation results for an FFL scanning open MPI system configuration to scan 3D field of view volume electronically. We analyze 3D imaging performance for various SNR levels at a low gradient value of 0.5 T/m that is relevant for clinical-size systems.

Deep Learned Super Resolution of System Matrices for Magnetic Particle Imaging

Magnetic Particle Imaging (MPI) is a new imaging technique that allows high resolution & high frame-rate imaging of magnetic nanoparticles (MNP). It relies on the nonlinear response of MNPs under a magnetic field. The imaging process can be modeled linearly, and then image reconstruction can be case as an inverse problem using a measured system matrix (SM). However, this calibration measurement is time consuming so it reduces practicality. In this study, we proposed a novel method for accelerating the SM calibration based on joint super-resolution (SR) and denoising of sensitivty maps (i.e., rows of SM). The proposed method is based on a deep convolutional neural network (CNN) architecture with residual-dense blocks. Model training was performed using noisy SM measurements simulated for varying MNP size and gradient strengths. Comparisons were performed against conventional low-resolution SM calibration, noisy high-resolution SM calibration, and bicubic upsampling of low-resolution SM. We show that the proposed method improves high-resolution SM recovery, and in turn leads to improved resolution and quality in subsequently reconstructed MPI images.

Concept for using magnetic particle imaging for intraoperative margin analysis in breast-conserving surgery

Breast-conserving surgery (BCS) is a commonly utilized treatment for early stage breast cancers but has relatively high reexcision rates due to post-surgical identification of positive margins. A fast, specific, sensitive, easy-to-use tool for assessing margins intraoperatively could reduce the need for additional surgeries, and while many techniques have been explored, the clinical need is still unmet. We assess the potential of Magnetic Particle Imaging (MPI) for intraoperative margin assessment in BCS, using a passively or actively tumor-targeted iron oxide agent and two hardware devices: a hand-held Magnetic Particle detector for identifying residual tumor in the breast, and a small-bore MPI scanner for quickly imaging the tumor distribution in the excised specimen. Here, we present both hardware systems and demonstrate proof-of-concept detection and imaging of clinically relevant phantoms.

The Reconstruction of Magnetic Particle Imaging: Current Approaches Based on the System Matrix

Magnetic particle imaging (MPI) is a novel non-invasive molecular imaging technology that images the distribution of superparamagnetic iron oxide nanoparticles (SPIONs). It is not affected by imaging depth, with high sensitivity, high resolution, and no radiation. The MPI reconstruction with high precision and high quality is of enormous practical importance, and many studies have been conducted to improve the reconstruction accuracy and quality. MPI reconstruction based on the system matrix (SM) is an important part of MPI reconstruction. In this review, the principle of MPI, current construction methods of SM and the theory of SM-based MPI are discussed. For SM-based approaches, MPI reconstruction mainly has the following problems: the reconstruction problem is an inverse and ill-posed problem, the complex background signals seriously affect the reconstruction results, the field of view cannot cover the entire object, and the available 3D datasets are of relatively large volume. In this review, we compared and grouped different studies on the above issues, including SM-based MPI reconstruction based on the state-of-the-art Tikhonov regularization, SM-based MPI reconstruction based on the improved methods, SM-based MPI reconstruction methods to subtract the background signal, SM-based MPI reconstruction approaches to expand the spatial coverage, and matrix transformations to accelerate SM-based MPI reconstruction. In addition, the current phantoms and performance indicators used for SM-based reconstruction are listed. Finally, certain research suggestions for MPI reconstruction are proposed, expecting that this review will provide a certain reference for researchers in MPI reconstruction and will promote the future applications of MPI in clinical medicine.