Partial FOV Center Imaging (PCI): A Robust X-Space Image Reconstruction for Magnetic Particle Imaging

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.

Magnetic particle imaging deblurring with dual contrastive learning and adversarial framework

Magnetic particle imaging (MPI) is an emerging medical imaging technique that has high sensitivity, contrast, and excellent depth penetration. In MPI, x-space is a reconstruction method that transforms the measured voltages into particle concentrations. The reconstructed native image can be modeled as a convolution of the magnetic particle concentration with a point-spread function (PSF). The PSF is one of the important parameters in deconvolution. However, accurately measuring or modeling the PSF in the hardware used for deconvolution is challenging due to the various environment and magnetic particle relaxation. The inaccurate PSF estimation may lead to the loss of the content structure of the MPI image, especially in low gradient fields. In this study, we developed a Dual Adversarial Network (DAN) with patch-wise contrastive constraint to deblur the MPI image. This method can overcome the limitations of unpaired data in data acquisition scenarios and remove the blur around the boundary more effectively than the common deconvolution method. We evaluated the performance of the proposed DAN model on simulated and real data. Experimental results confirmed that our model performs favorably against the deconvolution method that is mainly used for deblurring the MPI image and other GAN-based deep learning models.

Harmonic dependence of thermal magnetic particle imaging

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

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.

Anisotropic edge-preserving network for resolution enhancement in unidirectional Cartesian magnetic particle imaging

Objective. Magnetic particle imaging (MPI) is a novel imaging modality. It is crucial to acquire accurate localization of the superparamagnetic iron oxide nanoparticles distributions in MPI. However, the spatial resolution of unidirectional Cartesian trajectory MPI exhibits anisotropy, which blurs the boundaries of MPI images and makes precise localization difficult. In this paper, we propose an anisotropic edge-preserving network (AEP-net) to alleviate the anisotropic resolution of MPI.Methods. AEP-net resolve the resolution anisotropy by constructing an asymmertic convolution. To recover the edge information, we design the uncertainty region module. In addition, we evaluated the performance of the proposed AEP-net model by using simulations and experimental data.Results. The results show that the AEP-net model alleviates the anisotropy of the unidirectional Cartesian trajectory and preserves edge details in the MPI image. By comparing the visualization results and the metrics, we demonstrate that our method is superior to other methods.Significance. The proposed method produces accurate visualization in unidirectional Cartesian devices and promotes accurate quantization, which promote the biomedical applications using MPI.

Weighted sum of harmonic signals for direct imaging in magnetic particle imaging

Objective.Magnetic particle imaging (MPI) is a novel radiation-free medical imaging modality that can directly image superparamagnetic iron oxide tracers (SPIOs) with high sensitivity, temporal resolution, and good spatial resolution. The MPI reconstruction task can be formulated mathematically as a Fredholm integral problem, but the concrete inversion is not easily possible because of the particle dynamics or scanner issues. Measurement based system matrix inversion takes these factors into account, but prior measurement and calibration are time consuming.Approach.We proposed a direct imaging method based on the weighted sum of harmonic signals. The harmonic signals with spatial information are obtained by the short-time Fourier transform, and odd harmonic components are selected for recombination and then mapped to the sampling trajectory to image the concentration distribution of SPIOs. In addition, we adopt a normalized-weighted sum of harmonics to improve the resolution of the native image.Main results.The effectiveness of the proposed method is verified by simulation imaging experiments and our in-house scanner-based experiments. Quantitative evaluation results show that compared with traditional methods, the structural similarity improved by 48%, mean square error decreased by 88%, and signal-to-artifact ratio increased by 2.5 times.Significance.The proposed method can rapidly image the concentration distribution of nanoparticles without any prior calibration measurements and reduce the blur of MPI images without deconvolution, which has the potential to be implemented as a multi-patch imaging method in MPI.

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.

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.

Motion compensation for non-periodic dynamic tracer distributions in multi-patch magnetic particle imaging

Objective. While the spatial and temporal resolution of magnetic particle imaging is very high, the size of the field of view is limited due to physiological constraints. Multi-patch scans allow for covering larger areas by sequentially scanning smaller subvolumes, so-called patches. The visualization of tracer dynamics with a high temporal resolution are of particular interest in many applications, e.g. cardiovascular interventions or blood flow measurements. The reconstruction of non-periodic dynamic tracer distributions is currently realized by the reconstruction of a time-series of frames under the assumption of nearly static behavior during the scan of each frame. While this approach is feasible for limited velocities, it results in data gaps in multi-patch scans leading thus to artifacts for strong dynamics. In this article, we are aiming for the reconstruction of dynamic tracer concentrations with high velocities and the compensation of motion and multi-patch artifacts. Approach. We present a reconstruction method for dynamic tracer distributions using a dynamic forward model and representing the concentration within each voxel by a spline curve. The method is evaluated with simulated single- and multi-patch data. Main results. The dynamic model enables for the reconstruction of fast tracer dynamics from few frames and the spline approach approximates the missing data which reduces multi-patch artifacts. Significance. The presented method allows to compensate motion and multi-patch artifacts and to reconstruct fast dynamic tracer distributions with arbitrary motion patterns.

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.

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.

Characterization of noise and background signals in a magnetic particle imaging system

Magnetic Particle Imaging (MPI) is a novel technology, which opens new possibilities for promising biomedical applications. MPI uses magnetic fields to generate a specific response from magnetic nanoparticles (MNPs), to determine their spatial location non-invasively and without using ionizing radiation. One open challenge of MPI is to achieve further improvements in terms of sensitivity to translate the currently preclinical performed research into clinical applications. In this work, we study the noise and background signals of our preclinical MPI system, to identify and characterize disturbing signal contributions. The current limit of detection achieved with our device was determined previously to be 20 ng of iron. Based on the results presented in this work, we describe possible hardware and software improvements and estimate that the limit of detection could be lowered to about 200-400 pg. Additionally, a long-term analysis of the scanner performance over the last three years is presented, which proved to be an easy and effective way to monitor possible changes or damage of hardware components. All the presented results were obtained by analysing empty scanner measurements and the presented methodology can easily be adapted for different scanner types, to compare their performances.