The optimization of iron oxide nanoparticles as tracers for magnetic particle imaging (MPI) alongside the development of data acquisition equipment and image reconstruction techniques is crucial for the required improvements in image resolution and sensitivity of MPI scanners. We present a large-scale water-based synthesis of multicore superparamagnetic iron oxide nanoparticles stabilized with dextran (MC-SPIONs). We also demonstrate the preparation of single core superparamagnetic iron oxide nanoparticles in organic media, subsequently coated with a poly(ethylene glycol) gallic acid polymer and phase transferred to water (SC-SPIONs). Our aim was to obtain long-term stable particles in aqueous media with high MPI performance. We found that the amplitude of the third harmonic measured by magnetic particle spectroscopy (MPS) at 10 mT is 2.3- and 5.8-fold higher than Resovist for the MC-SPIONs and SC-SPIONs, respectively, revealing excellent MPI potential as compared to other reported MPI tracer particle preparations. We show that the reconstructed MPI images of phantoms using optimized multicore and specifically single-core particles are superior to that of commercially available Resovist, which we utilize as a reference standard, as predicted by MPS.

Quantitative mapping of gadoxetate uptake and excretion rates in liver cells has shown potential to significantly improve the management of chronic liver disease and liver cancer. Unfortunately, technical and clinical validation of the technique is currently hampered by the lack of data on gadoxetate relaxivity. The aim of this study was to fill this gap by measuring gadoxetate relaxivity in liver tissue, which approximates hepatocytes, in blood, urine and bile at magnetic field strengths of 1.41, 1.5, 3, 4.7 and 7 T. Measurements were performed ex vivo in 44 female Mrp2 knockout rats and 30 female wild‐type rats who had received an intravenous bolus of either 10, 25 or 40 μmol/kg gadoxetate. T1 was measured at 37 ± 3°C on NMR instruments (1.41 and 3 T), small‐animal MRI (4.7 and 7 T) and clinical MRI (1.5 and 3 T). Gadolinium concentration was measured with optical emission spectrometry or mass spectrometry. The impact on measurements of gadoxetate rate constants was determined by generalizing pharmacokinetic models to tissues with different relaxivities. Relaxivity values (L mmol⁻¹ s⁻¹) showed the expected dependency on tissue/biofluid type and field strength, ranging from 15.0 ± 0.9 (1.41) to 6.0 ± 0.3 (7) T in liver tissue, from 7.5 ± 0.2 (1.41) to 6.2 ± 0.3 (7) T in blood, from 5.6 ± 0.1 (1.41) to 4.5 ± 0.1 (7) T in urine and from 5.6 ± 0.4 (1.41) to 4.3 ± 0.6 (7) T in bile. Failing to correct for the relaxivity difference between liver tissue and blood overestimates intracellular uptake rates by a factor of 2.0 at 1.41 T, 1.8 at 1.5 T, 1.5 at 3 T and 1.2 at 4.7 T. The relaxivity values derived in this study can be used retrospectively and prospectively to remove a well‐known bias in gadoxetate rate constants. This will promote the clinical translation of MR‐based liver function assessment by enabling direct validation against reference methods and a more effective translation between in vitro findings, animal models and patient studies.

Background: Many translational MR biomarkers derive from measurements of the water proton longitudinal relaxation rate R-1, but evidence for between-site reproducibility of R-1 in small-animal MRI is lacking. Objective: To assess R-1 repeatability and multi-site reproducibility in phantoms for preclinical MRI. Methods: R-1 was measured by saturation recovery in 2% agarose phantoms with five nickel chloride concentrations in 12 magnets at 5 field strengths in 11 centres on two different occasions within 1-13 days. R-1 was analysed in three different regions of interest, giving 360 measurements in total. Root-mean-square repeatability and reproducibility coefficients of variation (CoV) were calculated. Propagation of reproducibility errors into 21 translational MR measurements and biomarkers was estimated. Relaxivities were calculated. Dynamic signal stability was also measured. Results: CoV for day-to-day repeatability (N = 180 regions of interest) was 2.34% and for between-centre reproducibility (N = 9 centres) was 1.43%. Mostly, these do not propagate to biologically significant between centre error, although a few R-1-based MR biomarkers were found to be quite sensitive even to such small errors in R-1, notably in myocardial fibrosis, in white matter, and in oxygen-enhanced MRI. The relaxivity of aqueous Ni2+ in 2% agarose varied between 0.66 s(-1) mM(-1) at 3 T and 0.94 s(-1) mM(-1) at 11.7T. Interpretation: While several factors affect the reproducibility of R-1-based MR biomarkers measured pre-clinically, between-centre propagation of errors arising from intrinsic equipment irreproducibility should in most cases be small. However, in a few specific cases exceptional efforts might be required to ensure R-1-reproducibility.

Effective crisis response requires both situational awareness and efficient coordination of logistics resources across multiple agencies. While Command, Control, Communications and Intelligence / Incident Management Systems (C3I/IMS) provide a Common Operational Picture (COP) and vehicle dispatching, they lack embedded optimisation capabilities for dynamic routing and allocation. This work operationalises logistics optimisation within a real C3I/IMS by introducing a module that formulates response logistics as a Multi-Depot Capacitated Vehicle Routing Problem with heterogeneous fleet characteristics, inventory coupling, compatibility constraints, and explicit time components. The module functions as an event-driven intelligence layer, automatically generating allocation plans, geospatial routes, Estimated Time of Arrival (ETA) predictions, and diagnostic outputs. A synthetic civil protection scenario demonstrates system integration and workflow coherence. The contribution lies in embedding optimisation-driven logistics intelligence within a multi-agency command-and-control environment, improving response efficiency, transparency, and interoperability, while also supporting preparedness through integrated simulation capabilities.

Effective disaster response requires coordinated multi-agency action under time pressure, uncertainty, and degraded conditions. Digital technologies improve situational awareness and decision-making, but operational adoption remains challenged by fragmented data, limited interoperability, and misalignment with responder practices. This paper presents work-in-progress insights from the SYNERGISE project, using a socio-technical, exercise-driven approach to develop and evaluate a Novel Integrated Toolkit for Collaborative Response and Enhanced Situational Awareness. The toolkit integrates robotic platforms, wearable sensors, augmented reality, explainable AI, and logistics optimisation, within a Command, Control, Communications, and Intelligence / Incident Management System. Common Operational Picture provides role-tailored multi-source information through interfaces supporting coordination across headquarters and field teams. Field deployments highlight the potential of system-of-systems architectures to improve coordination, responder safety, and operational awareness, while exposing dependencies on resilient communication, training, and effective information filtering. The paper outlines methodological insights, challenges, and implications for future evaluations of collaborative disaster response technologies.