International Journal on Magnetic Particle Imaging (IJMPI)
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Synthetic Antiferromagnet Disk Particles for Hyperthermia Applications
No full textHeat generation from chemically synthesized superparamagnetic nanoparticles remains limited by the low magnetization of the typically used oxidic materials, the wide particle size distribution, and the narrow shape of their magnetic hysteresis loop.
The overcome these limits, synthetic antiferromagnet magnetic disk particles (SAF MDP) consisting of two ferromagnetic (F) layers separated by a non-magnetic layer were designed. The geometry and magnetic system parameters were optimized via micromagnetic modeling to obtain an antiferromagnetically coupled (zero moment) ground state and an abrupt switching into a ferromagnetically aligned state in an applied field HAF?F to maximize the hysteretic loss.
The magnetic multilayer was sputter-deposited onto a 50nm-thick Ge sacrificial layer on a silicon wafer. Self—assembled polysterene spheres served as an etch mask for the successive nanopatterning of disk-shaped islands. These were then detached from the supporting wafer by dissolution of the Ge layer.
The magnetic properties of the SAF MDP analyzed by vibrating sample and Kerr magnetometry, high-resolution in-field magnetic force microscopy closely matched the design goals and micromagnetic simulation results. A turn-on/turn-off magnetism of the SAF MDP and a hysteretic loss close to the theoretical limit given by the magnetic material with the highest saturation magnetization and a perfectly rectangular hysteresis loop could be demonstrated. Experiments mapping the hysteretic loss of SAF MDP suspensions for different operation conditions are presently underway.  
MPIMeasurements.jl: An Extensible Julia Framework for Composable Magnetic Particle Imaging Devices
No full textMagnetic particle imaging (MPI) is a pre-clinical imaging modality, whose system design is still evolving, in particular towards human studies and clinical use. Therefore, many MPI scanners are custom-made distributed systems, both on the hard- and the software side. In this work we present the open-source Julia framework MPIMeasurements.jl, which implements a composable representation of imaging systems. It also offers flexible data structures that allow the implementation of specific imaging protocols, such as online/offline measurements, repeated measurements and system matrix calibrations. %that are reusable across systems. The project is designed to be expanded to new systems through community development and component reuse. To showcase the versatility of the software package, we give an overview of four very different MPI systems, which were realized with MPIMeasurements.jl
Safe and Rapid 3D Imaging: Upgrade of a Human-Sized Brain MPI System
No full textMagnetic Particle Imaging hardware has reached human scale and thus patient safety questions and clinical application scenarios are in the focus of current research. In this work, we present a safe real-time 3D MPI systemfor cerebral applications. High voltages are avoided to ensure patient safety by a low voltage-high current transmit coil design. The developed 2D drive-field generator generates a field-free-point trajectory in the sagittal xz-planethat is shifted by a dynamic selection-field sequence along the y-axis. The scanner generates 3D images with 4 frames/second and allows for direct visualization of the clinically preferred transversal yz-plane, which is crucialfor future brain examinations. Advanced reconstruction techniques reach a system sensitivity of 4 ?gFe with respect to the iron mass in a sensitivity study
System-matrix based reconstruction in magnetic particle imaging
No full textIn this work system-matrix based reconstruction in the context of magnetic particle imaging (MPI) is performed using 2D simulated system matrices and phantoms to study the effects of different approaches in the reconstruction chain. As an example, multi color reconstruction at different temperatures is chosen to show how the approach itself changes the reconstruction. It will be shown that a simple and trivial detail has important consequences for the reconstruction algorithms not only halving the number of unknowns but also giving commonly used solvers for the minimization problem a chance to converge to similar solutions
An active cancellation method to improve the sensitivity for amplitude modulation magnetic particle imaging
No full textSince the amplitude modulation magnetic particle imaging use the cancellation method to remove the direct feedthrough interference of the sinusoidal excitation field so that the sensitivity of MPI system depend on the amount of feedthrough interference of excitation field. The conventional cancellation method used single power to apply to two excitation coils, it will issue a difference about amplitude and phase of receive coil and cancellation coil. An active cancellation method uses two independent powers for two excitation coils, and controls two powers to reduce the difference of amplitude, phase between receive coil and cancellation coil so that the feedthrough interference of excitation field will reduce significant than conventional cancellation method. The works proposed an active cancelation method to suppress fundamental frequency from the excitation field but keep full dynamic range of harmonics from particles which contributes to both better image reconstruction and sensitivity of MPI system. The method included two excitation coils, one receive coil and one cancelation coil. After they are calibrated in mechanical by placing the coils, an electrical calibration is automatically controlled by two different power supplies in the sufficient accuracy. As a results, the total received signal after the active cancellation method is reduced nearly by 6dB / factor 2, from 50mV to 25mV comparing with the conventional cancellation method. Moreover, the phase difference about 71.645°between the excitation coils is adjusted for the cancellation
From bench to bedside: does a human-sized MPI scanner work with endoprosthesis of the hip and knee?
No full textInvestigation of influence of metallic endoprostheses on MPI functionality in a human-sized MPI scanner. No relevant impairment of imaging from the presence of endoprosthesis could be found
Sparsifying system matrices by combined usage of compressed sensing and extrapolation
No full textIn magnetic particle imaging the calibration step for a system matrix based reconstruction is very time and memory consuming. System matrices need to be measured not only in the physical field of view, but also in a bigger overscan region to avoid artifacts, especially in the case of multi-patch magnetic particle imaging. There are several methods to reduce the total number of voxels that need to be measured, e.g. compressed sensing and system matrix extrapolation. In this work, we show that a combination of these two methods is possible by using compressed sensing on a sparse sampling pattern only in the field of view and extrapolating the signal in the overscan region afterwards. We demonstrate on measured data, that such a combination gives superior results than using only compressed sensing on the whole system matrix. This is clearly manifested in the reduction of noise in the reconstruction result, especially when using a high undersampling factor
3D System Matrix Calibration by Using Coil Information and Transformer
No full textSystem Matrix-based image reconstruction approach requires a time-consuming calibration measurement. Existing methods such as compressed sensing and deep learning-based methods treat each row of the system matrix as independent data sample and lack the ability to modelling the relationships between SM rows. We firstly propose to model SM row relationships by the coil position and frequency value, which can be regarded as the additional and multimodal information. we propose a transformer-based neural network for 3D fast SM calibration, which encodes the information of coil position and frequency value into SM with self-attention mechanism in transformer
Automated MPI segmentation in X-space and calibration to quantify iron concentration in a tracer distribution
No full textMagnetic particle imaging (MPI) is a new, radiation-free medical imaging modality that relies on the non-linear magnetization response of superparamagnetic iron oxide nanoparticles (SPIONs) to reconstruct their concentration distribution with high sensitivity and medical safety. Current quantification methods for region of interest (ROI) are inadequate and are usually outlined manually or using deep learning methods. We propose two new models for ROI selection based on machine learning, one is the K-means++-based threshold-inflated image segmentation model and the other is the image segmentation model based on MPI simulation and SVM. We have developed an accurate quantification of 2D MPI images and established the calibration curve to predict the corresponding iron content based on the MPI image
System Matrix Recovery based on Adaptive Sparse Domain Selection in Magnetic Particle Imaging
No full textSystem matrix (SM) acquisition is an important research content in magnetic particle imaging (MPI). In order to avoid a redundant and time-consuming calibration process, sparse representation of SM has been successfully used for SM recovery based on compressed sensing (CS) framework in the past decade. The success of sparse representation owes to the fact that SM is intrinsically sparse in some domain, such as DCT, DFT and wavelet. However, these sparse domains lack the adaptivity to local structures. Many dictionary learning methods aim at learning a universal and over-complete dictionary to represent various image structures, which gives us great inspiration. In SM recovery based on the CS framework, each row of SM can also be regarded as an image. Considering that the contents can vary significantly across different patches in each image, we propose to learn a set of compact sub-dictionaries from high quality SM image patches. The known SM image patches can be grouped into many clusters. A compact sub-dictionary can be learned for each cluster due to the similar patterns of each cluster. Since the adaptive selection of sub-dictionaries can better represent a given patch, the entire image can be recovered more accurately than using a universal dictionary. Then, by solving the inverse problem based on sparse regularization method, the recovered high-quality SM can be obtained. It is expected that this study will increase the practicability of MPI in biomedical applications and promote the development of MPI in the future