Artifact-suppressing reconstruction of strongly interacting objects in X-ray near-field holography without a spatial support constraint.

The phase problem is a well known ill-posed reconstruction problem of coherent lens-less microscopic imaging, where only the squared magnitude of a complex wavefront is measured by a detector while the phase information of the wave field is lost. To retrieve the lost information, common algorithms rely either on multiple data acquisitions under varying measurement conditions or on the application of strong constraints such as a spatial support. In X-ray near-field holography, however, these methods are rendered impractical in the setting of time sensitive in situ and operando measurements. In this paper, we will forego the spatial support constraint and propose a projected gradient descent (PGD) based reconstruction scheme in combination with proper preprocessing and regularization that significantly reduces artifacts for refractive reconstructions from only a single acquired hologram without a spatial support constraint. We demonstrate the feasibility and robustness of our approach on different data sets obtained at the nano imaging endstation of P05 at PETRA III (DESY, Hamburg) operated by Helmholtz-Zentrum Hereon.

Resonant inductive coupling network for human-sized magnetic particle imaging.

In magnetic particle imaging, a field-free region is maneuvered throughout the field of view using a time-varying magnetic field known as the drive-field. Human-sized systems operate the drive-field in the kHz range and generate it by utilizing strong currents that can rise to the kA range within a coil called the drive field generator. Matching and tuning between a power amplifier, a band-pass filter, and the drive-field generator is required. Here, for reasons of safety in future human scanners, a symmetrical topology and a transformer called an inductive coupling network are used. Our primary objectives are to achieve floating potentials to ensure patient safety while attaining high linearity and high gain for the resonant transformer. We present a novel systematic approach to the design of a loss-optimized resonant toroid with a D-shaped cross section, employing segmentation to adjust the inductance-to-resistance ratio while maintaining a constant quality factor. Simultaneously, we derive a specific matching condition for a symmetric transmit-receive circuit for magnetic particle imaging. The chosen setup filters the fundamental frequency and allows simultaneous signal transmission and reception. In addition, the decoupling of multiple drive field channels is discussed, and the primary side of the transformer is evaluated for maximum coupling and minimum stray field. Two prototypes were constructed, measured, decoupled, and compared to the derived theory and method-of-moment based simulations.

Solving the MPI reconstruction problem with automatically tuned regularization parameters.

In the field of medical imaging, magnetic particle imaging (MPI) poses a promising non-ionizing tomographic technique with high spatial and temporal resolution. In MPI, iterative solvers are used to reconstruct the particle distribution out of the measured voltage signal based on a system matrix. The amount of regularization needed to reconstruct an image of good quality differs from measurement to measurement, depending on the MPI system and the measurement settings. Finding the right choice for the three major parameters controlling the regularization is commonly done by hand and requires time and experience. In this work, we study the reduction to a single regularization parameter and propose a method that enables automatic reconstruction. The method is qualitatively and quantitatively validated on several MPI data sets showing promising results.

Real-time multi-contrast magnetic particle imaging for the detection of gastrointestinal bleeding.

Gastrointestinal bleeding, as a potentially life-threatening condition, is typically diagnosed by radiation-based imaging modalities like computed tomography or more invasively catheter-based angiography. Endoscopy enables examination of the upper gastrointestinal tract and the colon but not of the entire small bowel. Magnetic Particle Imaging (MPI) enables non-invasive, volumetric imaging without ionizing radiation. The aim of this study was to evaluate the feasibility of detecting gastrointestinal bleeding by single- and multi-contrast MPI using human-sized organs. A 3D-printed small bowel phantom and porcine small bowel specimens were prepared with a defect within the bowel wall as the source of a bleeding. For multi-contrast MPI, the bowel lumen was filled with an intestinal tracer representing an orally administered tracer. MPI was performed to evaluate the fluid exchange between the vascular compartment of the bowel wall and the lumen while a blood pool tracer was applied. Leakage of the blood pool tracer was observed to the bowel lumen. Multi-contrast MPI enabled co-registration of both tracers at the same location within the bowel lumen indicating gastrointestinal bleeding. Single- and multi-contrast MPI are feasible to visualize gastrointestinal bleeding. Therefore, MPI might emerge as a useful tool for radiation-free detection of bleeding within the entire gastrointestinal tract.

Saline bolus for negative contrast perfusion imaging in magnetic particle imaging.

Objective.Magnetic particle imaging (MPI) is capable of high temporal resolution measurements of the spatial distribution of magnetic nanoparticles and therefore well suited for perfusion imaging, which is an important tool in medical diagnosis. Perfusion imaging in MPI usually requires a fresh bolus of tracer material to capture the key signal dynamics. Here, we propose a method to decouple the imaging sequence from the injection of additional tracer material, without further increasing the administered iron dose in the body with each image.Approach.A bolus of physiological saline solution without any particles (negative contrast) diminishes the steady-state concentration of a long-circulating tracer during passage. This depression in the measured concentration contributes to the required contrast dynamics. The presence of a long-circulating tracer is therefore a prerequisite to obtain the negative contrast. As a quantitative tracer based imaging method, the signal is linear in the tracer concentration for any location that contains nanoparticles and zero in the surrounding tissue which does not provide any intrinsic signal. After tracer injection, the concentration over time (positive contrast) can be utilized to calculate dynamic diagnostic parameters like perfusion parameters in vessels and organs. Every acquired perfusion image thus requires a new bolus of tracer with a sufficiently large iron dose to be visible above the background.Main results.Perfusion parameters are calculated based on the time response of the proposed negative bolus and compared to a positive bolus. Results from phantom experiments show that normalized signals from positive and negative boli are concurrent and deviations of calculated perfusion maps are low.Significance.Our method opens up the possibility to increase the total monitoring time of a future patient by utilizing a positive-negative contrast sequence, while minimizing the iron dose per acquired image.

Extrapolation of System Matrices in Magnetic Particle Imaging.

Magnetic particle imaging exploits the non-linear magnetization of superparamagnetic iron-oxide particles to generate a tomographic image in a defined field-of-view. For reconstruction of the particle distribution, a time-consuming calibration step is required, in which system matrices get measured using a robot. To achieve artifact-free images, system matrices need to cover not only the field-of-view but also a larger area around it. Especially for large measurements- inevitable for future clinical application- this leads to long calibration time and high consumption of persistent memory. In this work, we analyze the signal in the outer part of the system matrix and motivate the usage of extrapolation methods to computationally expand the system matrix after restricting the calibration to the field-of-view. We propose a suitable extrapolation method and show its applicability on measured 2D and 3D data. In doing so, we achieve a considerable reduction of calibration time and consumption of persistent memory while preserving an artifact-free result.

First Dedicated Balloon Catheter for Magnetic Particle Imaging.

Vascular interventions are a promising application of Magnetic Particle Imaging enabling a high spatial and temporal resolution without using ionizing radiation. The possibility to visualize the vessels as well as the devices, especially at the same time using multi-contrast approaches, enables a higher accuracy for diagnosis and treatment of vascular diseases. Different techniques to make devices MPI visible have been introduced so far, such as varnish markings or filling of balloons. However, all approaches include challenges for in vivo applications, such as the stability of the varnishing or the visibility of tracer filled balloons in deflated state. In this contribution, we present for the first time a balloon catheter that is molded from a granulate incorporating nanoparticles and can be visualized sufficiently in MPI. Computed tomography is used to show the homogeneous distribution of particles within the material. Safety measurements confirm that the incorporation of nanoparticles has no negative effect on the balloon. A dynamic experiment is performed to show that the inflation as well as deflation of the balloon can be imaged with MPI.

Particle interactions and their effect on magnetic particle spectroscopy and imaging.

Signal stability is crucial for an accurate diagnosis via magnetic particle imaging (MPI). However, MPI-tracer nanoparticles frequently agglomerate during their in vivo applications leading to particle interactions altering the signal. Here, we investigate the influence of such magnetic coupling phenomena on the MPI signal. We prepared Zn0.4Fe2.6O4 nanoparticles by flame spray synthesis and controlled their inter-particle distance by varying SiO2 coating thickness. The silica shell affected the magnetic properties indicating stronger particle interactions for a smaller inter-particle distance. The SiO2-coated Zn0.4Fe2.6O4 outperformed the bare sample in magnetic particle spectroscopy (MPS) in terms of signal/noise, however, the shell thickness itself only weakly influenced the MPS signal. To investigate the importance of magnetic coupling effects in more detail, we benchmarked the MPS signal of the bare and SiO2-coated Zn-ferrites against commercially available PVP-coated Fe3O4 nanoparticles in water and PBS. PBS is known to destabilize nanoparticle colloids mimicking in vivo-like agglomeration. The bare and coated Zn-ferrites showed excellent signal stability, despite their agglomeration in PBS. We attribute this to their process-intrinsic aggregated morphology formed during their flame-synthesis, which generates an MPS signal only little affected by PBS. On the other hand, the MPS signal of commercial PVP-coated Fe3O4 strongly decreased in PBS compared to water, indicating strongly changed particle interactions. The relevance of this effect was further investigated in a human cell model. For PVP-coated Fe3O4, we detected a strong discrepancy between the particle concentration obtained from the MPS signal and the actual concentration determined via ICP-MS. The same trend was observed during their MPI analysis; while SiO2-coated Zn-ferrites could be precisely located in water and PBS, PVP-coated Fe3O4 could not be detected in PBS at all. This drastically limits the sensitivity and also general applicability of these commercial tracers for MPI and illustrates the advantages of our flame-made Zn-ferrites concerning signal stability and ultimately diagnostic accuracy.

System Matrix Based Reconstruction for Pulsed Sequences in Magnetic Particle Imaging.

Improving resolution and sensitivity will widen possible medical applications of magnetic particle imaging. Pulsed excitation promises such benefits, at the cost of more complex hardware solutions and restrictions on drive field amplitude and frequency. State-of-the-art systems utilize a sinusoidal excitation to drive superparamagnetic nanoparticles into the non-linear part of their magnetization curve, which creates a spectrum with a clear separation of direct feed-through and higher harmonics caused by the particles response. One challenge for rectangular excitation is the discrimination of particle and excitation signals, both broad-band. Another is the drive-field sequence itself, as particles that are not placed at the same spatial position, may react simultaneously and are not separable by their signal phase or shape. To overcome this potential loss of information in spatial encoding for high amplitudes, a superposition of shifting fields and drive-field rotations is proposed in this work. Upon close view, a system matrix approach is capable to maintain resolution, independent of the sequence, if the response to pulsed sequences still encodes information within the phase. Data from an Arbitrary Waveform Magnetic Particle Spectrometer with offsets in two spatial dimensions is measured and calibrated to guarantee device independence. Multiple sequence types and waveforms are compared, based on frequency space image reconstruction from emulated signals, that are derived from measured particle responses. A resolution of 1.0 mT (0.8 mm for a gradient of (-1.25,-1.25,2.5) Tm-1) in x- and y-direction was achieved and a superior sensitivity for pulsed sequences was detected on the basis of reference phantoms.

Fat Quantification in Dual-Layer Detector Spectral Computed Tomography: Experimental Development and First In-Patient Validation.

OBJECTIVES: Fat quantification by dual-energy computed tomography (DECT) provides contrast-independent objective results, for example, on hepatic steatosis or muscle quality as parameters of prognostic relevance. To date, fat quantification has only been developed and used for source-based DECT techniques as fast kVp-switching CT or dual-source CT, which require a prospective selection of the dual-energy imaging mode.It was the purpose of this study to develop a material decomposition algorithm for fat quantification in phantoms and validate it in vivo for patient liver and skeletal muscle using a dual-layer detector-based spectral CT (dlsCT), which automatically generates spectral information with every scan. MATERIALS AND METHODS: For this feasibility study, phantoms were created with 0%, 5%, 10%, 25%, and 40% fat and 0, 4.9, and 7.0 mg/mL iodine, respectively. Phantom scans were performed with the IQon spectral CT (Philips, the Netherlands) at 120 kV and 140 kV and 3 T magnetic resonance (MR) (Philips, the Netherlands) chemical-shift relaxometry (MRR) and MR spectroscopy (MRS). Based on maps of the photoelectric effect and Compton scattering, 3-material decomposition was done for fat, iodine, and phantom material in the image space.After written consent, 10 patients (mean age, 55 ± 18 years; 6 men) in need of a CT staging were prospectively included. All patients received contrast-enhanced abdominal dlsCT scans at 120 kV and MR imaging scans for MRR. As reference tissue for the liver and the skeletal muscle, retrospectively available non-contrast-enhanced spectral CT data sets were used. Agreement between dlsCT and MR was evaluated for the phantoms, 3 hepatic and 2 muscular regions of interest per patient by intraclass correlation coefficients (ICCs) and Bland-Altman analyses. RESULTS: The ICC was excellent in the phantoms for both 120 kV and 140 kV (dlsCT vs MRR 0.98 [95% confidence interval (CI), 0.94-0.99]; dlsCT vs MRS 0.96 [95% CI, 0.87-0.99]) and in the skeletal muscle (0.96 [95% CI, 0.89-0.98]). For log-transformed liver fat values, the ICC was moderate (0.75 [95% CI, 0.48-0.88]). Bland-Altman analysis yielded a mean difference of -0.7% (95% CI, -4.5 to 3.1) for the liver and of 0.5% (95% CI, -4.3 to 5.3) for the skeletal muscle. Interobserver and intraobserver agreement were excellent (>0.9). CONCLUSIONS: Fat quantification was developed for dlsCT and agreement with MR techniques demonstrated for patient liver and muscle. Hepatic steatosis and myosteatosis can be detected in dlsCT scans from clinical routine, which retrospectively provide spectral information independent of the imaging mode.

Influence of X-rays and gamma-rays on the mechanical performance of human bone factoring out intraindividual bone structure and composition indices.

Doses of irradiation above 25 â€‹kGy are known to cause irreversible mechanical decay in bone tissue. However, the impact of irradiation doses absorbed in a clinical setting on the mechanical properties of bone remains unclear. In daily clinical practice and research, patients and specimens are exposed to irradiation due to diagnostic imaging tools, with doses ranging from milligray to Gray. The aim of this study was to investigate the influence of irradiation at these doses ranges on the mechanical performance of bone independent of inter-individual bone quality indices. Therefore, cortical bone specimens (n â€‹= â€‹10 per group) from a selected organ donor were irradiated at doses of milligray, Gray and kilogray (graft tissue sterilization) at five different irradiation doses. Three-point bending was performed to assess mechanical properties in the study groups. Our results show a severe reduction in mechanical performance (work to fracture: 50.29 â€‹± â€‹11.49 Nmm in control, 14.73 â€‹± â€‹1.84 Nmm at 31.2 â€‹kGy p â€‹≤ â€‹0.05) at high irradiation doses of 31.2 â€‹kGy, which correspond to graft tissue sterilization or synchrotron imaging. In contrast, no reduction in mechanical properties were detected for doses below 30 â€‹Gy. These findings are further supported by fracture surface texture imaging (i.e. more brittle fracture textures above 31.2 â€‹kGy). Our findings show that high radiation doses (≥31.2 â€‹kGy) severely alter the mechanical properties of bone. Thus, irradiation of this order of magnitude should be taken into account when mechanical analyses are planned after irradiation. However, doses of 30 â€‹Gy and below, which are common for clinical and experimental imaging (e.g., radiation therapy, DVT imaging, CT imaging, HR-pQCT imaging, DXA measurements, etc.), do not alter the mechanical bending-behavior of bone.

Magnetic particle imaging for assessment of cerebral perfusion and ischemia.

Stroke is one of the leading worldwide causes of death and sustained disability. Rapid and accurate assessment of cerebral perfusion is essential to diagnose and successfully treat stroke patients. Magnetic particle imaging (MPI) is a new technology with the potential to overcome some limitations of established imaging modalities. It is an innovative and radiation-free imaging technique with high sensitivity, specificity, and superior temporal resolution. MPI enables imaging and diagnosis of stroke and other neurological pathologies such as hemorrhage, tumors, and inflammatory processes. MPI scanners also offer the potential for targeted therapies of these diseases. Due to lower field requirements, MPI scanners can be designed as resistive magnets and employed as mobile devices for bedside imaging. With these advantages, MPI could accelerate and improve the diagnosis and treatment of neurological disorders. This review provides a basic introduction to MPI, discusses its current use for stroke imaging, and addresses future applications, including the potential for clinical implementation. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.

Efficient Joint Estimation of Tracer Distribution and Background Signals in Magnetic Particle Imaging Using a Dictionary Approach.

Background signals are a primary source of artifacts in magnetic particle imaging and limit the sensitivity of the method since background signals are often not precisely known and vary over time. The state-of-the art method for handling background signals uses one or several background calibration measurements with an empty scanner bore and subtracts a linear combination of these background measurements from the actual particle measurement. This approach yields satisfying results in case that the background measurements are taken in close proximity to the particle measurement and when the background signal drifts linearly. In this work, we propose a joint estimation of particle distribution and background signal based on a dictionary that is capable of representing typical background signals. Reconstruction is performed frame-by-frame with minimal assumptions on the temporal evolution of background signals. Thus, even non-linear temporal evolution of the latter can be captured. Using a singular-value decomposition, the dictionary is derived from a large number of background calibration scans that do not need to be recorded in close proximity to the particle measurement. The dictionary is sufficiently expressive and represented by its principle components. The proposed joint estimation of particle distribution and background signal is expressed as a linear Tikhonov-regularized least squares problem, which can be efficiently solved. In phantom experiments it is shown that the method strongly suppresses background artifacts and even allows to estimate and remove the direct feed-through of the excitation field.

A wavelet-based sparse row-action method for image reconstruction in magnetic particle imaging.

PURPOSE: Magnetic particle imaging (MPI) is a preclinical imaging technique capable of visualizing the spatio-temporal distribution of magnetic nanoparticles. The image reconstruction of this fast and dynamic process relies on efficiently solving an ill-posed inverse problem. Current approaches to reconstruct the tracer concentration from its measurements are either adapted to image characteristics of MPI but suffer from higher computational complexity and slower convergence or are fast but lack in the image quality of the reconstructed images. METHODS: In this work we propose a novel MPI reconstruction method to combine the advantages of both approaches into a single algorithm. The underlying sparsity prior is based on an undecimated wavelet transform and is integrated into a fast row-action framework to solve the corresponding MPI minimization problem. RESULTS: Its performance is numerically evaluated against a classical FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) approach on simulated and real MPI data. The experimental results show that the proposed method increases image quality with significantly reduced computation times. CONCLUSIONS: In comparison to state-of-the-art MPI reconstruction methods, our approach shows better reconstruction results and at the same time accelerates the convergence rate of the underlying row-action algorithm.

MRIReco.jl: An MRI reconstruction framework written in Julia.

PURPOSE: The aim of this work is to develop a high-performance, flexible, and easy-to-use MRI reconstruction framework using the scientific programming language Julia. METHODS: Julia is a modern, general purpose programming language with strong features in the area of signal/image processing and numerical computing. It has a high-level syntax but still generates efficient machine code that is usually as fast as comparable C/C++ applications. In addition to the language features itself, Julia has a sophisticated package management system that makes proper modularization of functionality across different packages feasible. Our developed MRI reconstruction framework MRIReco.jl can therefore reuse existing functionality from other Julia packages and concentrate on the MRI-related parts. This includes common imaging operators and support for MRI raw data formats. RESULTS: MRIReco.jl is a simple to use framework with a high degree of accessibility. While providing a simple-to-use interface, many of its components can easily be extended and customized. The performance of MRIReco.jl is compared to the Berkeley Advanced Reconstruction Toolbox (BART) and we show that the Julia framework achieves comparable reconstruction speed as the popular C/C++ library. CONCLUSIONS: Modern programming languages can bridge the gap between high performance and accessible implementations. MRIReco.jl leverages this fact and contributes a promising environment for future algorithmic development in MRI reconstruction.

Simultaneous imaging of widely differing particle concentrations in MPI: problem statement and algorithmic proposal for improvement.

Magnetic particle imaging (MPI) is a tomographic imaging technique for determining the spatial distribution of superparamagnetic nanoparticles. Current MPI systems are capable of imaging iron masses over a wide dynamic range of more than four orders of magnitude. In theory, this range could be further increased using adaptive amplifiers, which prevent signal clipping. While this applies to a single sample, the dynamic range is severely limited if several samples with different concentrations or strongly inhomogeneous particle distributions are considered. One scenario that occurs quite frequently in pre-clinical applications is that a highly concentrated tracer bolus in the vascular system 'shadows' nearby organs with lower effective tracer concentrations. The root cause of the problem is the ill-posedness of the MPI imaging operator, which requires regularization for stable reconstruction. In this work, we introduce a simple two-step algorithm that increases the dynamic range by a factor of four. Furthermore, the algorithm enables spatially adaptive regularization, i.e. highly concentrated signals can be reconstructed with maximum spatial resolution, while low concentrated signals are strongly regularized to prevent noise amplification.

Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation.

OBJECTIVE: The aim is to evaluate whether smart worklist prioritization by artificial intelligence (AI) can optimize the radiology workflow and reduce report turnaround times (RTATs) for critical findings in chest radiographs (CXRs). Furthermore, we investigate a method to counteract the effect of false negative predictions by AI-resulting in an extremely and dangerously long RTAT, as CXRs are sorted to the end of the worklist. METHODS: We developed a simulation framework that models the current workflow at a university hospital by incorporating hospital-specific CXR generation rates and reporting rates and pathology distribution. Using this, we simulated the standard worklist processing "first-in, first-out" (FIFO) and compared it with a worklist prioritization based on urgency. Examination prioritization was performed by the AI, classifying eight different pathological findings ranked in descending order of urgency: pneumothorax, pleural effusion, infiltrate, congestion, atelectasis, cardiomegaly, mass, and foreign object. Furthermore, we introduced an upper limit for the maximum waiting time, after which the highest urgency is assigned to the examination. RESULTS: The average RTAT for all critical findings was significantly reduced in all prioritization simulations compared to the FIFO simulation (e.g., pneumothorax: 35.6 min vs. 80.1 min; p < 0.0001), while the maximum RTAT for most findings increased at the same time (e.g., pneumothorax: 1293 min vs 890 min; p < 0.0001). Our "upper limit" substantially reduced the maximum RTAT in all classes (e.g., pneumothorax: 979 min vs. 1293 min/1178 min; p < 0.0001). CONCLUSION: Our simulations demonstrate that smart worklist prioritization by AI can reduce the average RTAT for critical findings in CXRs while maintaining a small maximum RTAT as FIFO. KEY POINTS: • Development of a realistic clinical workflow simulator based on empirical data from a hospital allowed precise assessment of smart worklist prioritization using artificial intelligence. • Employing a smart worklist prioritization without a threshold for maximum waiting time runs the risk of false negative predictions of the artificial intelligence greatly increasing the report turnaround time. • Use of a state-of-the-art convolution neural network can reduce the average report turnaround time almost to the upper limit of a perfect classification algorithm (e.g., pneumothorax: 35.6 min vs. 30.4 min).

Design of a head coil for high resolution mouse brain perfusion imaging using magnetic particle imaging.

Magnetic particle imaging (MPI) is a novel and versatile imaging modality developing toward human application. When up-scaling to human size, the sensitivity of the systems naturally drops as the coil sensitivity depends on the bore diameter. Thus, new methods to push the sensitivity limit further have to be investigated to cope for this loss. In this paper a dedicated surface coil for mice is developed, improving the sensitivity in cerebral imaging applications. Similar to magnetic resonance imaging the developed surface coil improves the sensitivity due to the closer vicinity to the region of interest. With the developed surface coil presented in this work, it is possible to image tracer samples containing only 896 pg[Formula: see text] and detect even small vessels and anatomical structures within a wild type mouse model. As current sensitivity measures require a tracer system a new method for determining a sensitivity measure without this requirement is presented and verified to enable comparison between MPI receiver systems.

Monitoring Intracranial Cerebral Hemorrhage Using Multicontrast Real-Time Magnetic Particle Imaging.

Magnetic particle imaging (MPI) is an innovative radiation-free tomographic imaging method providing excellent temporal resolution, contrast, sensitivity, and safety. Mobile human MPI prototypes suitable for continuous bedside monitoring of whole-brain perfusion have been developed. However, for the clinical translation of MPI, a crucial gap in knowledge still remains: while MPI can visualize the reduction in blood flow and tissue perfusion in cerebral ischemia, it is unclear whether MPI works in intracranial hemorrhage. Our objective was to investigate the capability of MPI to detect intracranial hemorrhage in a murine model. Intracranial hemorrhage was induced through the injection of collagenase into the striatum of C57BL/6 mice. After the intravenous infusion of a long-circulating MPI-tailored tracer consisting of superparamagnetic iron oxides, we detected the intracranial hemorrhage in less than 3 min and could monitor hematoma expansion in real time. Multicontrast MPI can distinguish tracers based on their physical characteristics, core size, temperature, and viscosity. By employing in vivo multicontrast MPI, we were able to differentiate areas of liquid and coagulated blood within the hematoma, which could provide valuable information in surgical decision making. Multicontrast MPI also enabled simultaneous imaging of hemorrhage and cerebral perfusion, which is essential in the care of critically ill patients with increased intracranial pressure. We conclude that MPI can be used for real-time diagnosis of intracranial hemorrhage. This work is an essential step toward achieving the clinical translation of MPI for point-of-care monitoring of different stroke subtypes.

Suppression of Motion Artifacts Caused by Temporally Recurring Tracer Distributions in Multi-Patch Magnetic Particle Imaging.

Magnetic particle imaging is a tracer based imaging technique to determine the spatial distribution of superparamagnetic iron oxide nanoparticles with a high spatial and temporal resolution. Due to physiological constraints, the imaging volume is restricted in size and larger volumes are covered by shifting object and imaging volume relative to each other. This results in reduced temporal resolution, which can lead to motion artifacts when imaging dynamic tracer distributions. A common source of such dynamic distributions are cardiac and respiratory motion in in-vivo experiments, which are in good approximation periodic. We present a raw data processing technique that combines data snippets into virtual frames corresponding to a specific state of the dynamic motion. The technique is evaluated on the basis of measurement data obtained from a rotational phantom at two different rotational frequencies. These frequencies are determined from the raw data without reconstruction and without an additional navigator signal. The reconstructed images give reasonable representations of the rotational phantom frozen in several different states of motion while motion artifacts are suppressed.