It has been shown that magnetic particle imaging (MPI), an imaging method suggested in 2005, is capable of measuring the spatial distribution of magnetic nanoparticles. Since the particles can be administered as biocompatible suspensions, this method promises to perform well as a tracerbased medical imaging technique. It is capable of generating realtime images, which will be useful in interventional procedures, without utilizing any harmful radiation. To obtain a signal from the administered superparamagnetic iron oxide (SPIO) particles, a sinusoidal changing external homogeneous magnetic field is applied. To achieve spatial encoding, a gradient field is superimposed. Conventional MPI works with a spatial encoding field that features a field free point (FFP). To increase sensitivity, an improved spatial encoding field, featuring a field free line (FFL) can be used. Previous FFL scanners, featuring a one-dimensional excitation, could demonstrate the feasibility of the FFL-based MPI imaging process. In this work, an FFL-based MPI scanner is presented that features a two-dimensional excitation field and, for the first time, an electronic rotation of the spatial encoding field. Furthermore, the role of relaxation effects in MPI is starting to move to the center of interest. Nevertheless, no reconstruction schemes presented thus far include a dynamical particle model for image reconstruction. A first application of a model that accounts for relaxation effects in the reconstruction of MPI images is presented here in the form of a simplified, but well performing strategy for signal deconvolution. The results demonstrate the high impact of relaxation deconvolution on the MPI imaging process.
The Magnetic Particle Imaging (MPI) technology is a new imaging technique featuring an excellent possibility to detect iron oxide based nanoparticle accumulations in-vivo. The excitation of the particles and in turn the signal generation in MPI are achieved by using oscillating magnetic fields. In order to realize a spatial encoding, a field-free point (FFP) is steered through the field of view (FOV). Such a positioning of the FFP can thereby be achieved by mechanical or electromagnetical movement. Conventionally, the data acquisition path is either a planar two-dimensional or a three-dimensional FFP trajectory. Assuming human applications, the size of the FOV sampled by such trajectories is strongly limited by heating of the body and by nerve stimulations. In this work, a new approach acquiring MPI data based on the axial elongation of a two-dimensional FFP trajectory is proposed. It is shown that such an elongation can be used as a data acquisition path to significantly increase the acquisition speed, with negligible loss of spatial resolution.
Magnetic particle imaging (MPI) has emerged as a new imaging method with the potential of delivering images of high spatial and temporal resolutions and free of ionizing radiation. Recent studies demonstrated the feasibility of differentiation between signal-generating and non-signal-generating devices in Magnetic Particle Spectroscopy (MPS) and visualization of commercially available catheters and guide-wires in MPI itself. Thus, MPI seems to be a promising imaging tool for cardiovascular interventions. Several commercially available catheters and guide-wires were tested in this study regarding heating. Heating behavior was correlated to the spectra generated by the devices and measured by the MPI. The results indicate that each instrument should be tested separately due to the wide spectrum of measured temperature changes of signal-generating instruments, which is up to 85°C in contrast to non-signal-generating devices. Development of higher temperatures seems to be a limitation for the use of these devices in cardiovascular interventions.
Glioblastomas are highly malignant brain tumours. Mathematical models and their analysis provide a tool to support the understanding of the development of these tumours as well as the design of more effective treatment strategies. We have previously developed a multiscale model of glioblastoma progression that covers processes on the cellular and molecular scale. Here, we present a novel nutrient-dependent multiscale sensitivity analysis of this model that helps to identify those reaction parameters of the molecular interaction network that influence the tumour progression on the cellular scale the most. In particular, those parameters are identified that essentially determine tumour expansion and could be therefore used as potential therapy targets. As indicators for the success of a potential therapy target, a deceleration of the tumour expansion and a reduction of the tumour volume are employed. From the results, it can be concluded that no single parameter variation results in a less aggressive tumour. However, it can be shown that a few combined perturbations of two systematically selected parameters cause a slow-down of the tumour expansion velocity accompanied with a decrease of the tumour volume. Those parameters are primarily linked to the reactions that involve the microRNA-451 and the thereof regulated protein MO25.
Magnetic particle imaging (MPI) uses magnetic fields to visualize superparamagnetic iron oxide nanoparticles (SPIO). Today, Resovist(®) is still the reference SPIO for MPI. The objective of this study was to evaluate the in vivo blood half-life of two different types of Resovist (one from Bayer Pharma AG, and one from I'rom Pharmaceutical Co Ltd) in MPI.
Abstract In magnetic particle imaging (MPI), the spatial distribution of magnetic nanoparticles is determined by applying various static and dynamic magnetic fields. Due to the complex physical behavior of the nanoparticles, it is challenging to determine the MPI system matrix in practice. Since the first publication on MPI in 2005, different methods that rely on measurements or simulations for the determination of the MPI system matrix have been proposed. Some methods restrict the simulation to an idealized model to speed up data reconstruction by exploiting the structure of an idealized MPI system matrix. Recently, a method that processes the measurement data in x-space rather than frequency space has been proposed. In this work, we compare the different approaches for image reconstruction in MPI and show that the x-space and the frequency space reconstruction techniques are equivalent.
Digital tomosynthesis (DT) is a limited angle tomographic x-ray technique. It is an attractive low-dose alternative to computed tomography (CT) in many imaging applications. However, the DT dataset is incomplete, which leads to out-of-focus artifacts and limited axial resolution. In this paper, a novel dual-axis tilt acquisition geometry is proposed and evaluated. This geometry solves some issues in tomosynthesis with the traditional scanning geometry by scanning the object with a set of perpendicular arcs. In this geometry the acquisition in the additional perpendicular direction is done using a tiltable object supporting platform. The proposed geometry allows for capturing more singularities of the Radon transform, filling the Fourier space with more data and better approximating the Tuy-Smith conditions. In order to evaluate the proposed system, several studies have been carried out. To validate the simulation setup the performance of the traditional scanning geometry has been simulated and compared to known results from the literature. It has also been shown that the possible improvement of the image quality in the traditional geometry is limited. These limitations can be partially overcome by using the proposed dual-axis tilt geometry. The novel geometry is superior and with the same number of projections better reconstructed images can be obtained. All studies have been made using a software tomosynthesis simulator. A micro-CT reconstruction of a bone has been used as a software phantom. Simultaneous algebraic reconstruction has been used to reconstruct simulated projections. As a conclusion, acquiring data outside the standard arc allows for improving performance of musculoskeletal tomosynthesis. With the proposed dual-axis acquisition geometry a performance gain is achieved without an increase in dose and major modifications to the instrumentation of existing tomosynthesis devices.
Magnetic particle imaging (MPI) applies oscillating magnetic fields to determine the distribution of magnetic nanoparticles in vivo. Using a receive coil, the change of the particle magnetization can be detected. However, the signal induced by the nanoparticles is superimposed by the direct feedthrough interference of the sinusoidal excitation field, which couples into the receive coils. As the latter is several magnitudes higher, the extraction of the particle signal from the excitation signal is a challenging task.
Abstract Ferrofluids, which are stable, colloidal suspensions of single-domain magnetic nanoparticles, have a large impact on medical technologies like magnetic particle imaging (MPI), magnetic resonance imaging (MRI) and hyperthermia. Here, computer simulations promise to improve our understanding of the versatile magnetization dynamics of diluted ferrofluids. A detailed algorithmic introduction into the simulation of diluted ferrofluids will be presented. The algorithm is based on Langevin equations and resolves the internal and the external rotation of the magnetic moment of the nanoparticles, i.e., the Néel and Brown diffusion. The derived set of stochastic differential equations are solved by a combination of an Euler and a Heun integrator and tested with respect to Boltzmann statistics.
Abstract Magnetic particle imaging (MPI) is a novel tracer-based imaging method detecting the distribution of superparamagnetic iron oxide (SPIO) nanoparticles in vivo in three dimensions and in real time. Conventionally, MPI uses the signal emitted by SPIO tracer material located at a field free point (FFP). To increase the sensitivity of MPI, however, an alternative encoding scheme collecting the particle signal along a field free line (FFL) was proposed. To provide the magnetic fields needed for line imaging in MPI, a very efficient scanner setup regarding electrical power consumption is needed. At the same time, the scanner needs to provide a high magnetic field homogeneity along the FFL as well as parallel to its alignment to prevent the appearance of artifacts, using efficient radon-based reconstruction methods arising for a line encoding scheme. This work presents a dynamic FFL scanner setup for MPI that outperforms all previously presented setups in electrical power consumption as well as magnetic field quality.
The concept of a magnetic field-free line (FFL), with regard to the novel tomographic modality magnetic particle imaging (MPI), was recently introduced. Theoretical approaches predict the improvement of sensitivity of MPI by a factor of ten replacing the conventionally used field-free point (FFP) by a FFL. In this work, an experimental apparatus for generating an arbitrarily rotated and translated FFL field is described and tested.
In computed tomography (CT), metal objects in the region of interest introduce data inconsistencies during acquisition. Reconstructing these data results in an image with star shaped artifacts induced by the metal inconsistencies. To enhance image quality, the influence of the metal objects can be reduced by different metal artifact reduction (MAR) strategies. For an adequate evaluation of new MAR approaches a ground truth reference data set is needed. In technical evaluations, where phantoms can be measured with and without metal inserts, ground truth data can easily be obtained by a second reference data acquisition. Obviously, this is not possible for clinical data. Here, an alternative evaluation method is presented without the need of an additionally acquired reference data set.
The identification of corresponding landmarks across a set of training shapes is a prerequisite for statistical shape model (SSM) construction. We automatically establish 3D correspondence using one new and several known alternative approaches for consistent, shape-preserving, spherical parameterization. The initial correspondence determined by all employed methods is refined by optimizing a groupwise objective function. The quality of all models before and after optimization is thoroughly evaluated using several data sets of clinically relevant, anatomical objects of varying complexity. Correspondence quality is benchmarked in terms of the SSMs specificity and generalization ability, which are measured using different surface based distance functions. We find that our new approach performs best for complex objects. Furthermore, all new and previously published methods of our own allow for (i) building SSMs that are significantly better than the well-known SPHARM method, (ii) establishing quasi-optimal correspondence for low and moderately complex objects without additional optimization, and (iii) considerably speeding up convergence, thus, providing means for practical, fast, and accurate SSM construction.
The magnetic particle imaging method allows for the quantitative determination of spatial distributions of superparamagnetic nanoparticles in vivo. Recently, it was shown that the 1-D magnetic particle imaging process can be formulated as a convolution. Analyzing the width of the convolution kernel allows for predicting the spatial resolution of the method. However, this measure does not take into account the noise of the measured data. Furthermore, it does not consider a reconstruction step, which can increase the resolution beyond the width of the convolution kernel. In this paper, the spatial resolution of magnetic particle imaging is investigated by analyzing the modulation transfer function of the imaging process. An expression for the spatial resolution is derived, which includes the noise level and which is validated in simulations and experiments.
Signal encoding in magnetic particle imaging (MPI) is achieved by moving a field-free point (FFP) through the region of interest. One way to increase the sensitivity of the method is to scan the region of interest with a field-free line (FFL) instead of the FFP. Recently, the first feasible FFL coil setup was introduced. The purpose of this article is to improve the efficiency of the FFL coil geometry even further.
Magnetic particle imaging (MPI) is a new quantitative imaging technique capable of determining the spatial distribution of superparamagnetic nanoparticles at high temporal and spatial resolution. For reconstructing this spatial distribution, the particle dynamics and the scanner properties have to be known. To date, they are obtained in a tedious calibration procedure by measuring the magnetization response of a small delta sample shifted through the measuring field. Recently, first reconstruction results using a 1D model-based system function were published, showing comparable image quality as obtained with a measured system function. In this work, first 2D model-based reconstruction results of measured MPI data are presented.
Magnetic particle imaging (MPI) is a new imaging modality capable of imaging distributions of superparamagnetic nanoparticles with high sensitivity, high spatial resolution and, in particular, high imaging speed. The image reconstruction process requires a system function, describing the mapping between particle distribution and acquired signal. To date, the system function is acquired in a tedious calibration procedure by sequentially measuring the signal of a delta sample at the positions of a grid that covers the field of view. In this work, for the first time, the system function is calculated using a model of the signal chain. The modeled system function allows for reconstruction of the particle distribution in a 1-D MPI experiment. The approach thus enables fast generation of system functions on arbitrarily dense grids. Furthermore, reduction in memory requirements may be feasible by generating parts of the system function on the fly during reconstruction instead of keeping the complete matrix in memory.
A new computational multiscale model of glioblastoma growth is introduced. This model combines an agent-based model for representing processes on the cellular level with a molecular interaction network for each cell on the subcellular scale. The network is based on recently published work on the interaction of microRNA-451, LKB1 and AMPK in the regulation of glioblastoma cell migration and proliferation. We translated this network into a mathematical description by the use of 17 ordinary differential equations. In our model, we furthermore establish a link from the molecular interaction network of a single cell to cellular actions (e.g. chemotactic movement) on the microscopic level. First results demonstrate that the computational model reproduces a tumor cell development comparable to that observed in in vitro experiments.
The automatic segmentation of abdominal organs is a pre-requisite for many medical applications. Successful methods typically rely on prior knowledge about the to be segmented anatomy as it is for instance provided by means of active shape models (ASMs). Contrary to most previous ASM based methods, this work does not focus on individual organs. Instead, a more holistic approach that aims at exploiting inter-organ relationships to eventually segment a complex of organs is proposed. Accordingly, a flexible framework for automatic construction of multi-object ASMs is introduced, employed for coupled shape modeling, and used for co-segmentation of liver and spleen based on a new coupled shape/separate pose approach. Our first results indicate feasible segmentation accuracies, whereas pose decoupling leads to substantially better segmentation results and performs in average also slightly better than the standard single-object ASM approach.
In computed tomography imaging metal objects in the region of interest introduce inconsistencies during data acquisition. Reconstructing these data leads to an image in spatial domain including star-shaped or stripe-like artifacts. In order to enhance the quality of the resulting image the influence of the metal objects can be reduced. Here, a metal artifact reduction (MAR) approach is proposed that is based on a recomputation of the inconsistent projection data using a fully three-dimensional Fourier-based interpolation. The success of the projection space restoration depends sensitively on a sensible continuation of neighboring structures into the recomputed area. Fortunately, structural information of the entire data is inherently included in the Fourier space of the data. This can be used for a reasonable recomputation of the inconsistent projection data.
A novel hybrid continuum-discrete model to simulate tumour growth on a cellular scale is proposed. The lattice-based spatiotemporal model consists of reaction-diffusion equations that describe interactions between cancer cells and their microenvironment. The fundamental ingredients that are typically considered are the nutrient concentration, the extracellular matrix (ECM), and matrix degrading enzymes (MDEs). The in vivo processes are very complex and occur on different levels. This in turn leads to huge computational costs. The main contribution of the present work is therefore to describe the processes on the basis of simplified mathematical approaches, which, at the same time, depict realistic results to understand the biological processes. In this work, we discuss if we have to simulate the MDE or if the degraded matrix can be estimated directly with respect to the cancer cell distribution. Additionally, we compare the results for modelling tumour growth using the common and our simplified approach, thereby demonstrating the advantages of the proposed method. Therefore, we introduce variations of the positioning of the nutrient delivering blood vessels and use different initializations of the ECM. We conclude that the novel method, which does not explicitly model the matrix degrading enzymes, provides means for a straightforward and fast implementation for modelling tumour growth.
Magnetic Particle Imaging (MPI) is a recently invented tomographic imaging method that quantitatively measures the spatial distribution of a tracer based on magnetic nanoparticles. The new modality promises a high sensitivity and high spatial as well as temporal resolution. There is a high potential of MPI to improve interventional and image-guided surgical procedures because, today, established medical imaging modalities typically excel in only one or two of these important imaging properties. MPI makes use of the non-linear magnetization characteristics of the magnetic nanoparticles. For this purpose, two magnetic fields are created and superimposed, a static selection field and an oscillatory drive field. If superparamagnetic iron-oxide nanoparticles (SPIOs) are subjected to the oscillatory magnetic field, the particles will react with a non-linear magnetization response, which can be measured with an appropriate pick-up coil arrangement. Due to the non-linearity of the particle magnetization, the received signal consists of the fundamental excitation frequency as well as of harmonics. After separation of the fundamental signal, the nanoparticle concentration can be reconstructed quantitatively based on the harmonics. The spatial coding is realized with the static selection field that produces a field-free point, which is moved through the field of view by the drive fields. This article focuses on the frequency-based image reconstruction approach and the corresponding imaging devices while alternative concepts like x-space MPI and field-free line imaging are described as well. The status quo in hardware realization is summarized in an overview of MPI scanners.
A novel unconditionally stable, explicit numerical method is introduced to the field of modeling brain cancer progression on a tissue level together with an inverse problem (IP) based on optimal control theory that allows for automated model calibration with respect to observations in clinical imaging data.
Magnetic particle imaging has emerged as a new technique for the visualization and quantification of superparamagnetic iron oxide nanoparticles. It seems to be a very promising application for cardiovascular interventional radiology. A prerequisite for interventions is the artifact-free visualization of the required instruments and implants. Various commercially available catheters, guide wires, and a catheter experimentally coated with superparamagnetic iron oxide nanoparticles were tested regarding their signal characteristics using magnetic particle spectroscopy to evaluate their performance in magnetic particle imaging. The results indicate that signal-generating and non-signal-generating instruments can be distinguished. Furthermore, coating or loading non-signal-generating instruments with superparamagnetic iron oxide nanoparticles seems to be a promising approach, but optimized nanoparticles need yet to be developed.
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