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

  • Patryk Szwargulski*
    Patryk Szwargulski
    Section for Biomedical Imaging, and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, Germany
    Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, DE 21073, Germany
    *Email: [email protected]
  • Maximilian Wilmes
    Maximilian Wilmes
    Department of Neurology, and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, Germany
  • Ehsan Javidi
    Ehsan Javidi
    Department of Neurology, and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, Germany
    More by Ehsan Javidi
  • Florian Thieben
    Florian Thieben
    Section for Biomedical Imaging, and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, Germany
    Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, DE 21073, Germany
  • Matthias Graeser
    Matthias Graeser
    Section for Biomedical Imaging, and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, Germany
    Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, DE 21073, Germany
  • Martin Koch
    Martin Koch
    Institute of Medical Engineering, Universität zu Lübeck, Lübeck, DE 23562, Germany
    More by Martin Koch
  • Cordula Gruettner
    Cordula Gruettner
    Micromod Partikeltechnologie GmbH, Rostock, DE 18119, Germany
  • Gerhard Adam
    Gerhard Adam
    Department of Diagnostic and Interventional Radiology and Nuclear Medicine at the, and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, Germany
    More by Gerhard Adam
  • Christian Gerloff
    Christian Gerloff
    Department of Neurology, and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, Germany
  • Tim Magnus
    Tim Magnus
    Department of Neurology, and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, Germany
    More by Tim Magnus
  • Tobias Knopp
    Tobias Knopp
    Section for Biomedical Imaging, and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, Germany
    Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, DE 21073, Germany
    More by Tobias Knopp
  • , and 
  • Peter Ludewig*
    Peter Ludewig
    Department of Neurology, and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, Germany
    *Email: [email protected]
Cite this: ACS Nano 2020, 14, 10, 13913–13923
Publication Date (Web):September 17, 2020
https://doi.org/10.1021/acsnano.0c06326

Copyright © 2022 American Chemical Society. This publication is licensed under these Terms of Use.

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Abstract

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.

With 4.1 million cases and 2.8 million deaths per year, intracranial hemorrhage (ICH) is a devastating disease with high rates of mortality and disability. (1) High case fatality rates of 60% at one year and less than 20% of patients regaining functional independence result in an annual loss of 64.5 million years of healthy lives. (1) No specific therapy, either drug or surgical, has shown to significantly improve the outcome. (2) Rebleeding and hematoma expansion, in particular, are associated with poor long-term prognosis, and treatment remains challenging. Early neurological deterioration due to hematoma expansion occurs in more than two-thirds of patients within 48 h. (3) Frequent neurological examinations are necessary to detect clinical deterioration and signs of increased intracranial pressure at an early stage. Reliable and practical techniques that allow for the continuous and noninvasive bedside monitoring of intracranial hemorrhage and cerebral perfusion in these patients do not exist. Drawbacks of transcranial Doppler, (4) near-infrared spectroscopy, (5) and thermal diffusion probes (6) include the monitored volumes being highly regional or invasive implantations being necessary. Although computed tomography (CT) is very precise in detecting intracranial hemorrhage, it exposes the patients to a relatively high radiation dose, and renal disease is a contraindication for the use of CT contrast agents. A device that allows for continuous, radiation-free surveillance of the whole brain at the bedside would be of utmost interest for several pathologic conditions.
Magnetic particle imaging (MPI) is a young imaging technology that could close this healthcare gap. It is a radiation-free, no-tissue-background tomographic imaging method that directly detects superparamagnetic iron oxide particles (SPIOs) three-dimensionally with a superior temporal resolution of 21.54 ms for volumetric imaging. (7) In MPI, two magnetic fields are necessary for image generation. First, a homogeneous sinusoidal magnetic drive field instantaneously flips the SPIOs to induce a signal in the receive coils. For the spatial encoding of this signal, another strong magnetic field creates a field-free region (FFR). Only the particles in the vicinity of the FFR flip and induce a signal. After a raster scan of the FFR, the detected signal is assigned to the current location of the FFR and reconstructed into an image. Its combination of excellent contrast, sensitivity, spatial and temporal resolution, safety, and biocompatible tracers makes MPI a promising tool for vascular applications. (8) A prototype of a mobile human MPI head scanner, which can be operated at a patient’s bedside with a conventional power outlet, was recently developed. (8) It is highly sensitive and allows for the measurement of brain perfusion parameter maps over several days, considering clinically approved tracer dosages. This scanner could minimize time-consuming patient transports and improve treatment times. However, before deploying such a scanner on patients for day-to-day routine imaging in hospitals, it must be clarified whether MPI can detect all stroke subtypes.
While we have demonstrated that ischemic stroke imaging is possible with MPI, (9) it is unclear whether MPI can detect intracranial hemorrhage. Compared to imaging of ischemia, the detection of hemorrhage with MPI is more challenging as the tracer needs to accumulate at the bleeding site to be visible in MPI images. Previous work detecting traumatic brain injury with MPI did not simulate clinical circumstances; for example, the tracer was injected before the actual traumatic injury. (10) Furthermore, the earliest bleeding detection reported thus far was 60 min after injection of the tracer in an intestine bleeding model, (11) which would be too long in the case of a medical emergency. It remains to be answered whether the delay in bleeding detection is a fundamental issue of MPI or whether it can be improved. Should MPI fail to detect bleeding earlier, it is questionable whether this technique is applicable for everyday clinical use. Promisingly, the quality of the scanners, sequences, and image reconstruction algorithms has continuously been optimized. Today, even small amounts of iron in the lower nanogram iron range can be detected. (12) Besides high sensitivity and excellent temporal resolution, the spatial resolution has improved into the μm range. (13) In addition to these improvements, MPI can offer further technological innovations. Recent developments in MPI have enabled the possibility to distinguish tracers by their viscosity and visualize blood coagulation. (14,15) This multicontrast MPI technology has not been exploited for hemorrhage or other disease models thus far. It has the potential to determine whether bleeding has already clotted or whether any areas are still actively bleeding in combination with simultaneous cerebral perfusion imaging.
In this study, we investigated MPI’s capabilities to detect intracranial cerebral hemorrhage in a mouse model. We induced ICH through the intraparenchymal injection of collagenase, which causes active bleeding due to the disruption of the blood–brain barrier. Comparable to the human pathophysiology, this bleeding evolves over hours, mimicking secondary hematoma expansion in patients. After the injection of a long-circulating MPI-tailored SPIO tracer, we performed dynamic 3D-MPI scans with a high spatial and temporal resolution and monitored the animals over the course of 4 weeks. We demonstrate that multicontrast MPI can combine imaging of intracranial bleeding and cerebral perfusion and that MPI is sensitive and fast enough to be suitable as a future clinical imaging method.

Results and Discussion

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Detection of Intracranial Hemorrhage with MPI in C57Bl/6 Mice

The first challenge to overcome was the choice of the optimal tracer and application regimes for the rapid detection of the intracranial hemorrhage (experimental workflow: see Supplemental Figure S1; scanning parameters: Tables S1/S2). Various options were available, including short- and long-circulating tracers, which could be infused continuously or in a pulsed fashion. We started with three different setups: the tracer Perimag (Micromod GmbH, Germany) with a blood half-life of 30 min was either injected as a large bolus of 200 μL (cumulative 1000 μg iron, c[Fe] = 5 mg/mL); as smaller 50 μL boli (cumulative 243 μg iron in 300 μL, c[Fe] = 0.81 mg/mL) every 10 min for 1 h; or continuously injected for 1 h (cumulative 243 μg of iron in 300 μL, flow rate of 0.3 mL/h, c[Fe] = 0.81 mg/mL). Image reconstruction was performed online during the data acquisition using our self-developed MPI reconstruction toolbox. (16) The reconstruction took 8 s per frame, and the images were immediately visible to the investigators.
In all scenarios, we clearly detected the hemorrhage after 90 min (Supplemental Figure S2b/c). However, the earliest time point of the detection differed drastically between the groups: after the injection of a large bolus of Perimag, it took 10 min to visually detect the bleeding, 19 min in the group with repetitive boli, and 23 min in the group with continuous injection. Neither of these injection schemes achieved steady-state concentration of the tracer, despite the adjustment of the injection rate (Supplemental Figure S2a). Digital subtraction of the data to delineate the intracranial hemorrhage did not improve the results (Supplemental Figure S2c). Especially in the groups with repetitive and continuous injection, we saw a steady tracer accumulation in the blood pool, which made it impossible to apply the standard digital subtraction method by subtracting the first baseline images from the follow-up images. Instead, each pixel needed normalization by the data set’s image means to smoothen the concentration–time curves and simulate a steady-state tracer. Nevertheless, without a priori knowledge of the hemorrhage localization, it was difficult to detect the hemorrhage before 60 min in these animals.
Since the time to bleeding detection of the short-circulating tracer was insufficient for clinical workflow, we then investigated the use of long-circulating tracers with a more predictable steady-state signal. Our cooperation partner Micromod developed a PEGylated tracer with a blood half-life of 60 min (Synomag-D, 70 nm, surface: PEG 25000-OMe). Synomag-D provided excellent MPI properties, with a mean signal increase by a factor of 2 compared to Perimag and by a factor of 4 compared to the clinically approved ferucarbotran (Resovist; see Supplemental Figure S3). The long-circulating tracer drastically improved the results. After the injection of a similar amount of tracer (243 μg of iron in 200 μL, c[Fe] = 1.22 mg/mL), it only took 1.80 ± 0.3 min (n = 3) to detect a signal increase in the concentration–time curves (Figure 1b) and 2.63 ± 0.23 min to visualize the intracranial hemorrhage (Figure 1a).

Figure 1

Figure 1. Rapid detection of intracranial hemorrhage with MPI. After the intravenous injection of 200 μL of Synomag-D (243 μg iron, c[Fe] = 1.22 mg/mL), MPI scans were acquired dynamically with a temporal resolution of 21.54 s, resulting in 3D-(+t) MPI data sets. Digital subtraction with a positive-only filter was applied to the 3D-(+t) MPI data sets to delineate the hemorrhage. (a) Early detection and expansion of the intracranial hemorrhage after the tracer injection at several time points on fused MPI/MRI slices (a; upper row: coronal sections; middle row: transversal sections; lower row: sagittal sections; the white asterisk indicates the hemorrhage in MRI and MPI; data without digital subtraction are shown in Supplemental Figure S4; signals were normalized for better visibility). The signal information from these data sets was converted into a concentration–time curve on a voxel by pixel basis (b). Only 1.80 ± 0.3 min passed between the tracer bolus arrival in the brain and the hemorrhage detection. Bleeding continued up to 100 min (a, b). The extravasation of the tracer and expansion of the hemorrhage could be monitored in real time (see Supplemental Video V1). Color coding the tracer arrival time allowed for the differentiation of bleeding areas of different age (c; the needle tip of the syringe marks the collagenase injection site for hemorrhage induction).

To date, it is the fastest time ever recorded in bleeding detection with MPI and is on par with conventional imaging methods. We employed a tomographic imaging protocol with Lissajous-type sampling and system matrix-based reconstruction in our studies, which differs fundamentally from the projection imaging system, sampling, and reconstruction scheme used in other MPI bleeding studies. (10,11) Our approach was fully 3D and enabled precise and quantitative detection of coagulated blood and organ perfusion with a possible temporal resolution on the millisecond scale. Due to the good temporal resolution of our MPI scanner, the hemorrhage’s expansion could be monitored in real time (see Supplemental Video V1). In the first 20 min, the hemorrhage spread vigorously with a maximum bleeding rate of 0.061 ± 0.023 μL/s at around 4 min. Following this acute bleeding, we observed a phase of slower bleeding rates of 0.003 ± 0.002 μL/s and hematoma expansion up to 100 min after the collagenase injection. The expansion of the bleeding was color-coded by the tracer arrival times and allowed for visualizing and differentiating older and newer bleeding areas within the hemorrhage (Figure 1c). Between the last MPI image and the sacrificing of the animals for ex vivo magnetic resonance imaging (MRI), up to 1 h elapsed. In the collagenase model, hematoma growth can be observed up to 24 h, (17,18) which explains the small differences between the last MPI and MRI images.
To prove that the extravasated tracer generated the in vivo MPI signal within the intracranial hemorrhage, the brain and the hematoma were dissected and presented matching signals (Figure 2a). For the treatment of intracranial bleeding, it is necessary to determine the volume. Until now, the spatial resolution of the MPI was too poor to provide reliable volumetric measurements. By improving the hardware with organ-specific MPI coils, (13) the spatial resolution increased to 1 mm × 1 mm × 0.5 mm compared to initial experiments with a body coil, (12) which yielded about half the resolution. The new hardware allowed us to perform reliable volumetric measurements. A volume rendering of the bleeding in MRI and MPI and a comparison of MPI and histological sections showed comparable dimensions and shapes of the bleeding (Figure 2b/c; Supplemental Video V2). Volumetric measurements of the MPI signal and histological sections showed similar sizes (Figure 2c/d; MPI: 24.54 ± 2.06 mm3vs histology: 23.89 ± 2.42 mm3). The injection of the tracer itself did not increase bleeding volumes. Animals injected with 200 μL of Synomag-D (c[Fe] = 1.22 mg/mL) had similar lesion volumes when compared to control animals without tracer injection (Figure 2d; with tracer: 23.89 ± 2.42 mm3vs without tracer: 24.25 ± 1.23 mm3).

Figure 2

Figure 2. Volumetric measurements of intracranial hemorrhages with MPI. Analyzing the brain and the dissected intracranial hemorrhage ex vivo revealed that the in vivo MPI signal was generated by the intracranial hemorrhage (a: the upper panel shows a photograph of the ex vivo brain and the dissected hemorrhage, while the lower panel shows the corresponding MPI signal). The improved spatial resolution of MPI scanners allowed for calculating the hemorrhage volume. A 3D rendering of the MPI hemorrhage revealed a shape and size comparable to the MRI (b; left images: view from lateral; right images: view from below). The volume of bleeding calculated from the MPI data sets showed sizes comparable to histological sections of the same animals 24 h after the induction of the hemorrhage (c/d). The injection of the tracer did not lead to an increase in bleeding size (d; n.s.: not significant).

Multicontrast MPI to Distinguish the Physical States of the Tracer

An innovative feature of MPI is the possibility to distinguish tracers based on their physical characteristics, core size, temperature, and viscosity. The feasibility of this technique, called multicontrast MPI, has been demonstrated in simplified in vitro and in vivo experiments. (14,19,20) However, it has not yet been demonstrated whether multicontrast MPI has any benefit for diagnosis or treatment in disease models. Clinically, it would be desirable if multicontrast MPI could provide the possibility to distinguish coagulated from uncoagulated blood and to combine the monitoring of hemorrhage and whole-brain perfusion in vivo.
In the first step, we tested whether a fluid tracer in uncoagulated blood could be distinguished from a tracer that had extravasated and immobilized within the coagulated blood of the intracranial hemorrhage. To encode the physical behavior of these two specific tracer states, we performed calibration measurements to acquire two different MPI system matrices, (21,22) which were necessary for image reconstruction (see Supplemental Methods). To differentiate the immobilized tracer, we obtained a system matrix with concentrated particles immobilized with dental cement. We also acquired an alternative system matrix with dissected tissue of the intracranial hemorrhage after tracer injection. Both system matrices were highly similar, confirming that the coagulation of a blood–tracer mixture within the hemorrhage led to immobilization of the particles (see Supplemental Figure S5). It also showed that it would not be necessary to use biopsy tissue for calibration measurements in future studies. To differentiate the fluid tracer, we calibrated the system matrix with a liquid tracer sample. Both system matrices robustly separated liquid and immobilized tracer in in vitro measurements (Figure 3a/b). The liquid tracer’s system matrix showed minimal signal in the coagulated areas, which could also be caused by small amounts of liquid tracer, e.g., inside the blood vessels.

Figure 3

Figure 3. Multicontrast MPI for the differentiation of fluid and immobilized tracer. Fluid tracer and dissected hemorrhage tissue with immobilized tracer were used for calibration measurement to obtain MPI system matrices for image reconstruction. In an in vitro setting, multicontrast MPI distinguished liquid and immobilized tracer with the dedicated system matrices (a: design of phantom with the different samples of fluid and immobilized tracer and a negative control; b: the corresponding MPI signal of the phantom; the upper image shows an overlay of the reconstruction with the immobilized-dedicated (red) and the liquid-dedicated system matrix (gray), the lower panel shows the separated channels). Applying multicontrast MPI in vivo, we could detect areas with immobilized tracer reflecting areas of coagulated blood inside the hemorrhage (c; upper row: coronal sections of images reconstructed with the system matrix for immobilized tracer; middle row: images reconstructed with the system matrix for fluid tracer; lower row: digital subtraction images of the fluid tracer; bottom row: overlay of all three MPI reconstructions on the top of the corresponding MRI slice).

After establishing the technique in vitro, we applied multicontrast image reconstruction with the fluid and immobilized system matrices to our in vivo experiments (Figure 3; Supplemental Video V3). We clearly detected areas of immobilized tracer within the hemorrhage, which most likely represent areas of coagulated blood. These regions also matched the parts of the intracranial hemorrhage that occurred soon after the induction of the hemorrhage and should have clotted first (Figure 1c). Whereas the signal from the immobilized tracer was exclusive, the evaluation with the liquid tracer’s system matrix showed areas overlapping with the coagulated areas. This overlapping was reasonable since the in vivo measurements were performed in living animals with persisting blood circulation where both fluid and immobilized tracer coexist.

Simultaneous Multicontrast MPI of Brain Perfusion and Intracranial Hemorrhage

In the second step, we successfully further challenged multicontrast MPI by combining the two different tracers, Perimag and Synomag-D, to perform simultaneous monitoring of the hemorrhage and cerebral perfusion in vivo. After induction of bleeding and imaging with Synomag-D, a second short bolus with Perimag was injected. A few frames before the Perimag bolus were averaged and digitally subtracted from the following frames to remove the signal from residual liquid Synomag-D in the blood and delineate the Perimag bolus. The data were reconstructed with system matrices calibrated with immobilized Synomag-D and liquid Perimag. We could clearly distinguish the hemorrhage (immobilized Synomag-D) while the fluid Perimag bolus passed through the brain (Figure 4a; Supplemental Video V4). Multicontrast MPI allowed us to continuously observe the bleeding and, at the same time, calculate the perfusion parameter maps from the Perimag bolus (Figure 4b/c). The parameter maps for relative cerebral blood volume and flow (rCBV, rCBF) also showed reduced perfusion within the bleeding area, as expected (Figure 4c, red asterisk).

Figure 4

Figure 4. Multicontrast MPI for simultaneous monitoring of intracranial hemorrhage and cerebral perfusion. After the induction of the intracranial hemorrhage, the tracer Synomag-D (c[Fe] = 1.22 mg/mL) was injected for bleeding detection. Two hours later, we injected a 5 μL bolus of the tracer Perimag (c[Fe] = 57 mg/mL) for perfusion imaging with a temporal resolution of 21.54 ms. Images were reconstructed with the system matrix for immobilized Synomag-D and liquid Perimag (a; upper row: overlay of MPI and MRI data; middle row: immobilized tracer/Synomag-D/hemorrhage in red; lower row: fluid tracer/Perimag/cerebral perfusion in blue; the time labels correspond to the concentration–time curve in Figure 4b; the arrow in b marks the time point of the tracer injection). We could clearly detect the hemorrhage while the Perimag bolus passed through the brain in the images (a) and concentration–time curves (b; red line: hemorrhage/Synomag-D signal; blue line: Perimag bolus in the contralateral hemisphere; black dotted line: Perimag signal in a vein). Perfusion parameters maps (c; rCBV, rCBF) were derived from the concentration–time curves and showed decreased perfusion within the hemorrhage (c, red asterisk). A high concentration of the second tracer can shadow the first tracer. The phenomenon is present in the concentration–time curves, which illustrate a drop in the bleeding signal during the injection of Perimag (b).

Attention must be paid when administering the second tracer for perfusion imaging. Too much tracer for the perfusion bolus can overexpose the signal of the first tracer inside the hemorrhage, which is reflected by a drop in the hemorrhage’s signal during the Perimag injection (Figure 4b; see also Supplemental Figure S6). This can be explained by the fact that MPI is limited in the dynamic range it can resolve. Concentration differences of more than a factor of 50 can usually not be imaged simultaneously, since the higher concentration shadows the lower concentration even if they are spatially separated. In our experiments, we observed that multicontrast MPI was even more prone to the dynamic range limitations and that the separation of two different contrasts worked best if the two tracers had similar concentration.

Long-Term Imaging of Intracranial Hemorrhage with MPI

Superparamagnetic iron oxide particles do not cross the healthy blood–brain barrier, which breaks down multiple times during intracranial hemorrhage. Excessive extravasation of the tracer into the brain parenchyma is not desirable due to the potential side effects of the tracer deposition in the central nervous system. (23) To estimate how much tracer extravasated into the brain parenchyma, we stained the particles with Prussian blue 4 h after the injection. This method stains iron mostly in the ferric but not in the ferrous state. In the first hours after ICH, the iron from the red blood cells is bound to hemoglobin and in the ferrous state. It is therefore not recognized by Prussian blue staining, whereas Perimag or Synomag-D with iron in the ferric state (magnetite) was detected by this method (Figure 5a). (24) We did not see excessive amounts of particles, which were distributed evenly over the area of the intracranial hemorrhage. The Fe3+ amount measured with MPI was 827.3 ± 297.4 ng (n = 4) at day 1. We performed long-term imaging of the ICH with these animals. The MPI signal and the amount of iron quickly decreased. After 3 weeks, the amount of Fe3+ fell to 51.7 ± 41.5 ng (Figure 5b/c), which shows that the biocompatible tracer degraded and did not accumulate in the brain parenchyma (see Supplemental Figure S7).

Figure 5

Figure 5. Long-term MPI of the intracranial hemorrhage shows the degradation of the tracer. Animals were sacrificed 4 h after the induction of the hemorrhage and tracer injection. The amount of tracer inside the hemorrhage was evaluated through Prussian blue staining. This revealed homogeneous extravasation, whereas the staining was negative in animals without tracer injection or the contralateral side (a; upper image: tracer particles are stained in blue; middle image: ICH without injected tracer; lower image: contralateral hemisphere with injected tracer; scale bar: 100 μm). Imaging of the animal after the injection of the tracer (Synomag-D) was performed up to 28 days (b, c; n = 4 until day 23; one animal was imaged until day 28). Magnetic particle imaging signal intensities (b) and the amount of tracer (c) significantly decreased over 3–4 weeks.

Conclusions

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In this work, we have demonstrated that magnetic particle imaging reliably and quickly detected intracranial hemorrhage in a mouse model. Uncertainties about MPI’s ability to detect bleeding have arisen because MPI is a background-free imaging method, meaning that a tracer needs to be administered to generate an image. MPI can only detect intracranial hemorrhage if there is active bleeding and the tracer is leaking into the tissue. Preliminary work in the field of traumatic brain injury circumvented this problem by injecting the tracer before the injury. (10) We chose an approach that corresponds more to a clinical scenario, where the tracer is injected after the onset of symptoms and bleeding. In our model, we administered the tracer 30 min after the onset of the hemorrhage and quickly detected the ICH within minutes, as well as detected active bleeding up to 100 min. This is very much in line with human observations, where we find hematoma expansion in 73% of the patients within 48 h. (25) The secondary hematoma expansion leads to clinical deterioration and death. It is crucial to detect secondary bleeding early to treat these patients accordingly, such as through surgery. (26) To date, a reliable and fast method to allow for the continuous bedside monitoring of whole-brain perfusion does not exist. Patient surveillance has typically been noninvasive through clinical examination, but this cannot be carried out continuously. MPI can close this healthcare gap and enable noninvasive, permanent monitoring of patients and faster interventions in the case of deterioration. For this purpose, it is another advantage of MPI technology that the scanners can be built very small and can be designed as prehospital and bedside-operated devices. We recently published phantom measurements with a prototype of a mobile human head scanner that can be operated at the bedside with a conventional power outlet. (27)
To deploy MPI as a monitoring device, the rapid detection of rebleeding is a prerequisite. How fast MPI is in this scenario has not yet been conclusively clarified. Recent studies either did not address this issue in-depth (10) or detected initial signs of bleeding after more than 1 h. (11) With the right choice of tracer and tracer application, we detected intracranial hemorrhage within 2.5 min. With this rapid detection, MPI can now clearly compete with conventional imaging techniques, with average stroke examination times of 11–13 min for CT and MRI. (27) In addition, CT and MRI require transportation of the patients, while MPI could be carried out directly at the bedside. MPI might be an ideal technique to monitor hematoma expansion.
Another advantage of MPI is the multicontrast separation, which allows for differentiating tracers based on their viscosity and core size. In our experiments, multicontrast MPI distinguished areas of liquid and coagulated blood within the hematoma, which is not possible with other imaging techniques. Identifying stable areas of coagulated blood and areas of active bleeding is of great interest for surgical interventions, because operating during an active hemorrhage can worsen the outcome. An ultra-early surgical study was halted because there was a high rate of early rebleeding and mortality due to persistently bleeding vessels. (28) MPI could help define a specific time window when surgery is optimally performed depending upon the leaking vessel’s status. Minimally invasive catheter evacuation is a newer surgical approach. (29) A catheter is placed inside the hematoma, followed by intrahemorrhage thrombolysis and drainage of the blood clot over days. Multicontrast MPI could improve the placement of the catheter, monitor thrombolysis, and clot removal to achieve optimal hematoma evacuation.
Additionally, we have demonstrated that, by employing multicontrast MPI, it is possible to perform perfusion imaging while monitoring the intracranial hemorrhage. One of the critical complications of ICH is increased intracranial pressure due to the space restrictions imposed by the skull and hematoma expansion. Increased intracranial pressure leads to insufficient cerebral perfusion and increased secondary injury. With MPI, we could detect a drop in cerebral perfusion and adjust the therapy to control cerebral pressure. Another disease for which MPI could potentially be helpful is subarachnoid hemorrhage (SAH). SAH is usually caused by a rupture of an aneurysmatic cerebral vessel with bleeding into the subarachnoid space. This type of bleeding is also detectable by MPI (see Supplemental Figure S8). The complications that follow such bleeding are typically vasospasms of the cerebral arteries. These vasospasms can cause ischemic strokes due to decreased perfusion. With MPI, early detection and faster treatment of vasospasms with intra-arterial vasodilators would be possible.
In the case of intracranial hemorrhages, volumetric measurements are necessary, since surgical interventions may improve the outcome if the stroke volume exceeds 60 mL in combination with a significant midline shift. (30) Until now, volumetric evaluations of MPI were imprecise due to the limited spatial resolution on the order of >2 mm when imaging at feasible tracer concentrations in vivo. By incorporating new advantages of organ-specific receive coils, (12) we performed volumetric calculations from MPI data, which were within the same range as those obtained from high-resolution MRI and histological evaluations. This was possible due to the high signal–noise ratio (SNR) of the organ-specific receiving coil. This allowed us to use more frequency components in the reconstruction and to reduce the amount of regularization, both of which lead to an improved spatial resolution.
While we have demonstrated that bedside monitoring with MPI could be feasible in the near future, the prehospital application of such a scanner has limitations. Magnetic particle imaging could miss old bleedings because no tracer would leak into the hemorrhagic tissue. In this scenario, MPI could still detect a perfusion deficit, but it would not be able to differentiate between hemorrhagic and ischemic stroke. Therefore, the necessary distinction for a lysis therapy would not be possible.
Toxicity of the tracer materials is another topic that needs addressing. Tracers such as ferucarbotran have been used in patients with minor side effects similar to other tracer materials. (31) Concerns have emerged since cerebral gadolinium deposits have been detected in patients after repetitive applications. (32) Compared to gadolinium, MPI tracers have the advantage of being biodegradable. Accordingly, we found a steady decrease in the tracer signal in the brain parenchyma in our experiments, which can be the result of either local degradation or removal of the particles from the brain parenchyma. For MPI, the tracers require a superparamagnetic state. Degradation of the particles into smaller sized results in the full loss of the MPI signal. (33) Our data suggest that SPIOs get phagocytosed and digested by macrophages or microglia (see Supplemental Figure S7), followed by incorporation of the degraded iron into hemoglobin as observed by others. (34,35) Nevertheless, we cannot exclude that the particles degrade locally and remnants remain in the brain parenchyma. It is necessary to investigate further whether SPIOs get entirely removed from the brain parenchyma or increase neurotoxicity when the blood–brain barrier collapses. In our trials, both in stroke (23) and in bleeding, we did not observe any mortality after tracer injections, and the tracer did not increase bleeding sizes. Another advantage is that we found only small amounts (in the nanogram range) of tracer in the tissue. Due to the high sensitivity of our MPI scanner, including the organ-specific receive coil, even smaller amounts of tracer can be applied in the future.
A further optimization could be in the route of administration of the tracers. The tracers we used in our study can easily be encapsulated into red blood cells, as previously demonstrated. (36) Since red blood cells (RBCs) have a considerably long half-life, only a small amount of tracer needs to be applied. As long-circulating tracers, RBCs are probably also excellently suited for the detection of bleeding. Besides imaging, MPI could improve thrombolysis efficacy through drug targeting and localized hyperthermia, potentially decreasing thrombolysis times drastically. (37−39)
In summary, this work is an important step toward clinical MPI stroke and hemorrhage imaging. Combining our previous studies, (9,27) we have demonstrated that MPI can quickly detect both stroke subtypes, intracranial hemorrhage, and ischemic stroke, which are essential basic requirements for the use of MPI in clinical routine. By enabling the continuous monitoring of patients using mobile MPI scanners, this technology can substantially improve the care of stroke patients in the future.

Methods and Experimental

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1. Induction of Intracranial Hemorrhage

All animal experiments were approved by local animal care committees (Behörde für Lebensmittelsicherheit and Veterinärwesen Hamburg, No. 18/21). We conducted the experiments following the “Guide for the Care and Use of Laboratory Animals” published by the US National Institutes of Health (NIH Publication No. 83-123, revised 1996) and performed all procedures in accordance with the ARRIVE guidelines (http://www.nc3rs.org/ARRIVE). C57BL/6 mice (n = 27, see Supplemental Table S1) were purchased from the Jackson Laboratory (Bar Harbor, ME, USA). The mice were kept on a standard 12 h light/dark cycle, at a constant temperature (22 ± 2 °C), and with food and water adlibitum.
Intracranial hemorrhage was induced through the collagenase injection model. Mice were anesthetized with isoflurane (4% for induction; 2.5% for maintenance) in 100% oxygen. The mice were placed in a stereotaxic frame (Stoelting, 51500U), and a 1 cm long incision of the scalp was made over the midline. A cranial burr hole (0.9 mm) was drilled 2.3 mm lateral to the midline and 0.2 mm anterior to the bregma. Bacterial collagenase VII-S (Sigma, Germany, C0773-3KU) dissolved in saline was drawn into a 10 μL Hamilton syringe (Hamilton, 1701RN) connected to a 26-gauge needle (Hamilton, 26G, point style 4, 12°) and inserted into a motorized stereotaxic injector (Stoelting, integrated stereotaxic injector). The 26-gauge needle was slowly introduced 3.7 mm deep into the left striatum, and 0.5 μL of bacterial collagenase (0.075 units) was infused at a rate of 0.5 μL/min. The needle was left in place for 10 min and then slowly withdrawn. For the MPI measurements, the mouse was transferred to the MPI scanner, and anesthesia was maintained with 1.5% isoflurane in 100% O2. The vital parameters were monitored using an animal support unit (Minerve, Esternay, France). Body temperature was maintained throughout the procedure at 37 °C using a feedback-controlled heating device. For the tracer injection, a catheter (inner tube diameter 0.28 mm; Portex, Smiths Medical International Ltd., USA) was placed into the tail vein of the mouse.

2. MPI and MRI Measurements

We performed the main experiments using a preclinical tomographic MPI scanner (Bruker, Germany) that uses a field-free point (FFP) for spatial encoding. Magnetic particle imaging, presented by Gleich and Weizenecker in 2005, is a tracer-based imaging modality. Using external magnetic fields, the FFP was moved along a 3D imaging trajectory and captured the concentration in the vicinity of the FFP. The 3D Lissajous trajectories, which were used in this work, allowed for rapid sampling of 3D imaging volumes with a temporal resolution of 21.54 ms per imaging volume. To increase the detection limit of the commercial device, which was about 160 ng, we used a custom head coil that was mounted directly around the mouse head and improved the SNR by a factor of 200. The design of the coil allowed for easy positioning in the scanner bore without the necessity of additional fiducials. As tracer materials, we used Perimag and Synomag-D (Micromod GmbH, Germany). The injected tracer volume and concentration differed in the performed experiments and are summarized in Supplemental Table S1. In all cases, the tracer was injected with a programmable syringe pump (Aladdin; World Precisions Instruments, USA). The specific magnetic and nonmagnetic characteristics of Perimag and Synomag-D are listed in Supplemental Figure S3. We prepared the immobilized samples that were used for system matrix acquisition of the coagulated tracer channel by mixing Synomag-D with dental cement, which fixated the compound after a short contact time. For image reconstruction in MPI, additionally measured system matrices were needed. Since the bore size of the mouse head coil was too small for a dedicated calibration with a delta sample shifted to different positions in the field of view, we instead used a larger 3D mouse body coil (40 mm inner diameter) for calibration. Since both coils had a different transfer function, all measurements were corrected for the respective transfer function. This correction converted the signal into the magnetic moment of the particles, which is a receive coil independent measure. All measurements and system matrices were scanned with a selection field gradient of 2 T/m and an excitation amplitude of 12 mT in all three excitation directions. The covered field of view was 24 × 24 × 12 mm3. The system matrices were scanned with a 2 × 2 × 1 mm3 delta sample of each tracer material on a grid of 25 × 25 × 13 samples, covering a region of interest of 25 × 25 × 13 mm3.
As a morphological reference for the MPI measurements, we additionally performed post-mortem MRI measurements of the head using a 1 T MRI system (ICON, Bruker BioSpin MRI, Ettlingen, Germany). The heads were measured with two different sequences: first, a T1-weighted Flash sequence with TE = 15 ms, FA = 45°, TR = 444.34 ms, averages = 8, slice orientation: axial, readout direction: ventral-dorsal, 15 slices with a slice thickness of 1 mm, image size of 160 × 160 covering 26 × 26 mm2, and a scan time of 9 min 28 s. The second was a T1 Flash 3D high-resolution sequence with TE = 12 ms, FA = 30°, TR = 45 ms, averages = 4, slice orientation: coronal, readout direction: rostral-caudal, image size of 83 × 83 × 65 covering 24.9 × 24.9 × 19.5 mm3, and a scan time of 17 min 40 s. The MPI/MRI-related measurement and reconstruction parameters are summarized in Supplemental Tables S2/S3/S4.

3. MPI Image Reconstruction and Postprocessing

During the experiments, an online reconstruction tool was used to give direct feedback on the measurements, which was especially important for the multibolus experiments. A high-resolution parameter-tuned reconstruction, including background correction and receive channel and frequency filtering, was performed offline in a postprocessing step. To this end, the open-source MPI image reconstruction package MPIReco was used, (16) which provided both methods for single-contrast and multicontrast reconstruction. Prior to reconstruction, the system matrix was interpolated to a finer grid to achieve a spatial resolution of 1 × 1 × 0.5 mm3. For reconstruction, we used the iterative regularized Kaczmarz algorithm (see Supplemental Methods). For the calculation of perfusion parameter maps, we used the tool previously described. (9) Volume rendering, image registration, and bleeding size determination were performed using the open source software package Slicer 4.10.2. We determined the bleeding size in the MPI images by applying an intensity threshold of 8% of the maximum image intensity to neglect the image noise and calculate a binary mask matching the bleeding. The mask, in combination with the voxel size result, was then used to derive the bleeding size.
For the digital subtraction, a temporal maximum intensity projection (MIP) of the tracer bolus’ initial frames was rendered to generate a baseline image with a signal maximum in arteries and veins. The baseline MIP was subtracted from all subsequent frames. This approach suppressed the surrounding vessels in the images and allowed quick visualization of the hemorrhage signal increase. Since the tracer concentration in the vessels decreases over time, negative values were obtained in the digital subtraction image. These values were set to zero to segment the images of the bleeding region effectively. In the continuous injection scheme and the repetitive bolus case, an additional normalization of the signal intensity was necessary. To this end, each frame, including the reference frames, was normalized prior to subtraction.

4. Histological Analysis

Histological analyses were performed as described. (40) Briefly, mice were deeply anesthetized with isoflurane followed by perfusion through the left ventricle using 10 mL of phosphate-buffered saline and 50 mL of cold 4% paraformaldehyde (PFA). The brains were postfixed in 4% PFA overnight at 4 °C, followed by embedding in paraffin. Brains were cut into 10 μm coronal sections using a Leica microtome. Sections were stained with an iron stain kit according to the manufacturer’s instructions (iron stain kit, HT20-1KT, Sigma-Aldrich, MO, USA) and examined under a Leica DM5000B microscope.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsnano.0c06326.

  • Supplemental figures illustrating the experimental workflow, the advantages of long-circulation tracer, comparison of the MPI tracers Perimag and Synomag-D, the digital subtraction imaging, analysis of system matrices for multicontrast MPI, challenges of multicontrast MPI, phagocytosis of SPIOs by macrophages and microglia, MPI of subarachnoid hemorrhage; additional tables with the MPI and MRI parameters; additional equations used for multicontrast MPI image reconstruction, image postprocessing, and analysis. (PDF)

  • Supplemental Video V1: Real-time detection of intracranial hemorrhage with magnetic particle imaging (MPG)

  • Supplemental Video V2: Volumetric measurements of intracranial hemorrhage with magnetic particle imaging (MPG)

  • Supplemental Video V3: Differentiation of immobilized vs fluid tracer with multicontrast magnetic particle imaging (MPG)

  • Supplemental Video V4: Simultaneous imaging of hemorrhage and cerebral perfusion with multicontrast magnetic particle imaging (MPG)

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Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Authors
    • Patryk Szwargulski - Section for Biomedical Imaging, , and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, GermanyInstitute for Biomedical Imaging, Hamburg University of Technology, Hamburg, DE 21073, Germany Email: [email protected]
    • Peter Ludewig - Department of Neurology, , and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, GermanyOrcidhttp://orcid.org/0000-0001-9025-6402 Email: [email protected]
  • Authors
    • Maximilian Wilmes - Department of Neurology, , and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, Germany
    • Ehsan Javidi - Department of Neurology, , and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, Germany
    • Florian Thieben - Section for Biomedical Imaging, , and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, GermanyInstitute for Biomedical Imaging, Hamburg University of Technology, Hamburg, DE 21073, Germany
    • Matthias Graeser - Section for Biomedical Imaging, , and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, GermanyInstitute for Biomedical Imaging, Hamburg University of Technology, Hamburg, DE 21073, Germany
    • Martin Koch - Institute of Medical Engineering, Universität zu Lübeck, Lübeck, DE 23562, Germany
    • Cordula Gruettner - Micromod Partikeltechnologie GmbH, Rostock, DE 18119, Germany
    • Gerhard Adam - Department of Diagnostic and Interventional Radiology and Nuclear Medicine at the, , and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, Germany
    • Christian Gerloff - Department of Neurology, , and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, Germany
    • Tim Magnus - Department of Neurology, , and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, Germany
    • Tobias Knopp - Section for Biomedical Imaging, , and University Medical Center Hamburg−Eppendorf, Hamburg, DE 20246, GermanyInstitute for Biomedical Imaging, Hamburg University of Technology, Hamburg, DE 21073, Germany
  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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This work was supported by the “Forschungszentrums Medizintechnik Hamburg” (FMTHH) by the Hertie-Stiftung (Hertie Academy of Clinical Neuroscience), the German Research Foundation (DFG; grant numbers: GR 5287/2-1, KN 1108/7-1, DFG FOR 2879 [project LU 1924/1-1 and MA 4375/6-1], SFB 1328 [project A13]), and the “Hermann und Lily Schilling Stiftung”. This work was also supported by the BMBF under the frame of EuroNanoMed III (grant number: 13XP5060B, “Magnetise”).

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  • Abstract

    Figure 1

    Figure 1. Rapid detection of intracranial hemorrhage with MPI. After the intravenous injection of 200 μL of Synomag-D (243 μg iron, c[Fe] = 1.22 mg/mL), MPI scans were acquired dynamically with a temporal resolution of 21.54 s, resulting in 3D-(+t) MPI data sets. Digital subtraction with a positive-only filter was applied to the 3D-(+t) MPI data sets to delineate the hemorrhage. (a) Early detection and expansion of the intracranial hemorrhage after the tracer injection at several time points on fused MPI/MRI slices (a; upper row: coronal sections; middle row: transversal sections; lower row: sagittal sections; the white asterisk indicates the hemorrhage in MRI and MPI; data without digital subtraction are shown in Supplemental Figure S4; signals were normalized for better visibility). The signal information from these data sets was converted into a concentration–time curve on a voxel by pixel basis (b). Only 1.80 ± 0.3 min passed between the tracer bolus arrival in the brain and the hemorrhage detection. Bleeding continued up to 100 min (a, b). The extravasation of the tracer and expansion of the hemorrhage could be monitored in real time (see Supplemental Video V1). Color coding the tracer arrival time allowed for the differentiation of bleeding areas of different age (c; the needle tip of the syringe marks the collagenase injection site for hemorrhage induction).

    Figure 2

    Figure 2. Volumetric measurements of intracranial hemorrhages with MPI. Analyzing the brain and the dissected intracranial hemorrhage ex vivo revealed that the in vivo MPI signal was generated by the intracranial hemorrhage (a: the upper panel shows a photograph of the ex vivo brain and the dissected hemorrhage, while the lower panel shows the corresponding MPI signal). The improved spatial resolution of MPI scanners allowed for calculating the hemorrhage volume. A 3D rendering of the MPI hemorrhage revealed a shape and size comparable to the MRI (b; left images: view from lateral; right images: view from below). The volume of bleeding calculated from the MPI data sets showed sizes comparable to histological sections of the same animals 24 h after the induction of the hemorrhage (c/d). The injection of the tracer did not lead to an increase in bleeding size (d; n.s.: not significant).

    Figure 3

    Figure 3. Multicontrast MPI for the differentiation of fluid and immobilized tracer. Fluid tracer and dissected hemorrhage tissue with immobilized tracer were used for calibration measurement to obtain MPI system matrices for image reconstruction. In an in vitro setting, multicontrast MPI distinguished liquid and immobilized tracer with the dedicated system matrices (a: design of phantom with the different samples of fluid and immobilized tracer and a negative control; b: the corresponding MPI signal of the phantom; the upper image shows an overlay of the reconstruction with the immobilized-dedicated (red) and the liquid-dedicated system matrix (gray), the lower panel shows the separated channels). Applying multicontrast MPI in vivo, we could detect areas with immobilized tracer reflecting areas of coagulated blood inside the hemorrhage (c; upper row: coronal sections of images reconstructed with the system matrix for immobilized tracer; middle row: images reconstructed with the system matrix for fluid tracer; lower row: digital subtraction images of the fluid tracer; bottom row: overlay of all three MPI reconstructions on the top of the corresponding MRI slice).

    Figure 4

    Figure 4. Multicontrast MPI for simultaneous monitoring of intracranial hemorrhage and cerebral perfusion. After the induction of the intracranial hemorrhage, the tracer Synomag-D (c[Fe] = 1.22 mg/mL) was injected for bleeding detection. Two hours later, we injected a 5 μL bolus of the tracer Perimag (c[Fe] = 57 mg/mL) for perfusion imaging with a temporal resolution of 21.54 ms. Images were reconstructed with the system matrix for immobilized Synomag-D and liquid Perimag (a; upper row: overlay of MPI and MRI data; middle row: immobilized tracer/Synomag-D/hemorrhage in red; lower row: fluid tracer/Perimag/cerebral perfusion in blue; the time labels correspond to the concentration–time curve in Figure 4b; the arrow in b marks the time point of the tracer injection). We could clearly detect the hemorrhage while the Perimag bolus passed through the brain in the images (a) and concentration–time curves (b; red line: hemorrhage/Synomag-D signal; blue line: Perimag bolus in the contralateral hemisphere; black dotted line: Perimag signal in a vein). Perfusion parameters maps (c; rCBV, rCBF) were derived from the concentration–time curves and showed decreased perfusion within the hemorrhage (c, red asterisk). A high concentration of the second tracer can shadow the first tracer. The phenomenon is present in the concentration–time curves, which illustrate a drop in the bleeding signal during the injection of Perimag (b).

    Figure 5

    Figure 5. Long-term MPI of the intracranial hemorrhage shows the degradation of the tracer. Animals were sacrificed 4 h after the induction of the hemorrhage and tracer injection. The amount of tracer inside the hemorrhage was evaluated through Prussian blue staining. This revealed homogeneous extravasation, whereas the staining was negative in animals without tracer injection or the contralateral side (a; upper image: tracer particles are stained in blue; middle image: ICH without injected tracer; lower image: contralateral hemisphere with injected tracer; scale bar: 100 μm). Imaging of the animal after the injection of the tracer (Synomag-D) was performed up to 28 days (b, c; n = 4 until day 23; one animal was imaged until day 28). Magnetic particle imaging signal intensities (b) and the amount of tracer (c) significantly decreased over 3–4 weeks.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsnano.0c06326.

    • Supplemental figures illustrating the experimental workflow, the advantages of long-circulation tracer, comparison of the MPI tracers Perimag and Synomag-D, the digital subtraction imaging, analysis of system matrices for multicontrast MPI, challenges of multicontrast MPI, phagocytosis of SPIOs by macrophages and microglia, MPI of subarachnoid hemorrhage; additional tables with the MPI and MRI parameters; additional equations used for multicontrast MPI image reconstruction, image postprocessing, and analysis. (PDF)

    • Supplemental Video V1: Real-time detection of intracranial hemorrhage with magnetic particle imaging (MPG)

    • Supplemental Video V2: Volumetric measurements of intracranial hemorrhage with magnetic particle imaging (MPG)

    • Supplemental Video V3: Differentiation of immobilized vs fluid tracer with multicontrast magnetic particle imaging (MPG)

    • Supplemental Video V4: Simultaneous imaging of hemorrhage and cerebral perfusion with multicontrast magnetic particle imaging (MPG)


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