L1 data fitting for robust reconstruction in magnetic particle imaging: quantitative evaluation on Open MPI dataset

Magnetic particle imaging is an emerging quantitative imaging modality, exploiting the unique nonlinear magnetization phenomenon of superparamagnetic iron oxide nanoparticles for recovering the concentration. Traditionally the reconstruction is formulated into a penalized least-squares problem with nonnegativity constraint, and then solved using a variant of Kaczmarz method which is often stopped early after a small number of iterations. Besides the phantom signal, measurements additionally include a background signal and a noise signal. In order to obtain good reconstructions, a preprocessing step of frequency selection to remove the deleterious influences of the noise is often adopted. In this work, we propose a complementary pure variational approach to noise treatment, by viewing highly noisy measurements as outliers, and employing the l1 data fitting, one popular approach from robust statistics. When compared with the standard approach, it is easy to implement with a comparable computational complexity. Experiments with a public domain dataset, i.e., Open MPI dataset, show that it can give accurate reconstructions, and is less prone to noisy measurements, which is illustrated by quantitative (PSNR / SSIM) and qualitative comparisons with the Kaczmarz method. We also investigate the performance of the Kaczmarz method for small iteration numbers quantitatively

Radon-based Image Reconstruction for MPI using a continuously rotating FFL

Magnetic particle imaging is a relatively new tracer-based medical imaging technique exploiting the non-linear magnetization response of magnetic nanoparticles to changing magnetic fields. If the data are generated by using a field-free line, the sampling geometry resembles the one in computerized tomography. Indeed, for an ideal field-free line rotating only in between measurements it was shown that the signal equation can be written as a convolution with the Radon transform of the particle concentration. In this work, we regard a continuously rotating field-free line and extend the forward operator accordingly. We obtain a similar result for the relation to the Radon data but with two additive terms resulting from the additional time-dependencies in the forward model. We jointly reconstruct particle concentration and corresponding Radon data by means of total variation regularization yielding promising results for synthetic data.Comment: YRM & CSE Workshop on Modeling, Simulation & Optimization of Fluid Dynamic Applications 202

Unique Compact Representation of Magnetic Fields using Truncated Solid Harmonic Expansions

Precise knowledge of magnetic fields is crucial in many medical imaging applications like magnetic resonance imaging or magnetic particle imaging (MPI) as they are the foundation of these imaging systems. For the investigation of the influence of field imperfections on imaging, a compact and unique representation of the magnetic fields using real solid spherical harmonics, which can be obtained by measuring a few points of the magnetic field only, is of great assistance. In this manuscript, we review real solid harmonic expansions as a general solution of Laplace's equation including an efficient calculation of their coefficients using spherical t-designs. We also provide a method to shift the reference point of an expansion by calculating the coefficients of the shifted expansion from the initial ones. These methods are used to obtain the magnetic fields of an MPI system. Here, the field-free-point of the spatial encoding field serves as unique expansion point. Lastly, we quantify the severity of the distortions of the static and dynamic fields in MPI by analyzing the expansion coefficients.Comment: 25 page

System Characterization of a Human-Sized 3D Real-Time Magnetic Particle Imaging Scanner for Cerebral Applications

Since the initial patent in 2001, the Magnetic Particle Imaging (MPI) community has been striving to develop an MPI scanner suitable for human applications. Numerous contributions from different research fields, regarding tracer development, reconstruction methods, hardware engineering, and sequence design have been employed in pursuit of this objective. In this work, we introduce and thoroughly characterize an improved head-sized MPI scanner with an emphasis on human safety. The scanner is operated by open-source software that enables scanning, monitoring, analysis, and reconstruction, designed to be handled by end users. Our primary focus is to present all technical components of the scanner, with the ultimate objective to investigate brain perfusion imaging in phantom experiments. We have successfully achieved full 3D single- and multi-contrast imaging capabilities at a frame rate of 4 Hz with sufficient sensitivity and resolution for brain applications. To assess system characterization, we devised sensitivity, resolution, perfusion, and multi-contrast experiments, as well as field measurements and sequence analysis. The acquired images were captured using a clinically approved tracer and suitable magnetic field strengths, while adhering to the established human peripheral nerve stimulation thresholds. This advanced scanner holds potential as a tomographic imager for diagnosing conditions such as ischemic stroke or intracranial hemorrhage in environments lacking electromagnetic shielding. Furthermore, due to its low power consumption it may have the potential to facilitate long-term monitoring within intensive care units for various applications.Comment: 22 pages, 9 figure

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

Quantification of Lipoprotein Uptake in Vivo Using Magnetic Particle Imaging and Spectroscopy

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

Quantification of Lipoprotein Uptake in Vivo Using Magnetic Particle Imaging and Spectroscopy

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