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Item type:Publication, Access status: with files , Data-driven motion compensation in HR-pQCT(Institute of Physics (IOP), 2026-10-16); ; ; ; Abstract Objective. High-resolution peripheral quantitative computed tomography enables the assessment of bone mineral density and three-dimensional microstructure of peripheral limbs. Due to its long scanning time, it is especially susceptible to patient induced motion artifacts. The goal of this study is to compensate for such artifacts without requiring excessive computation time, while still avoiding the hallucination of erroneous structures. Approach. This work proposes a machine learning-based approach for the rapid estimation of rigid motion parameters directly from raw projection data. The model is trained on patient scans with motion simulated as a single, instantaneous jump. We demonstrate the performance and generalization of the method on simulated and experimentally acquired data, benchmarking our results against two recent baselines. Main results. The proposed method greatly reduced the amount of motion artifacts in both simulated and experimental data. On the simulated test data, the median structural similarity index measure (SSIM) for the reconstruction results could be improved from 0.62 to 0.88. Similarly, the SSIM for the experimental data improved from 0.58–0.71 to 0.74–0.78. At the tibia site that was used for training, motion artifacts were successfully reduced to such an extent that 92 % of previously inaccessible scans could be reconsidered for clinical evaluation. For the unseen radius site, a clinical recovery rate of 56 % could be achieved. Predicting the motion parameters for a measurement required less than 200 ms on consumer hardware. Significance. The proposed approach has the potential to enable the reuse of scans that were previously discarded for clinical evaluation while preventing generative hallucinations by design. Consequently, eliminating the need for repeated scans would reduce patient radiation exposure and clinical workflow overhead.Publicationtype: Journal Article13 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Efficient Chebyshev reconstruction for the anisotropic equilibrium model in magnetic particle imaging(Institute of Physics (IOP), 2026-10-15); ; ; ; Objective. Magnetic particle imaging (MPI) is a tomographic imaging modality capable of real-time, high-sensitivity mapping of superparamagnetic iron oxide nanoparticles. Model-based image reconstruction provides an alternative to conventional methods that rely on a measured system matrix, eliminating the need for laborious calibration measurements. Nevertheless, model-based approaches must account for the complexities of the imaging chain to maintain high image quality. This work investigates and accelerates an adapted direct Chebyshev reconstruction (DCR) method that accounts for the physical effect of magnetic nanoparticle anisotropy. Approach. The adapted DCR method employs weighted Chebyshev polynomials in the frequency domain and incorporates a spatially variant deconvolution step to compensate for magnetic anisotropy effects. The method is evaluated on five experimental phantoms and compared with model-based and measured system matrix reconstructions. In addition, an efficient approximation of the spatially variant deconvolution is introduced to reduce computational runtime and memory consumption while maintaining reconstruction accuracy. The proposed approach is further investigated on simulated three-dimensional data to assess its scalability to volumetric MPI. Main results. The adapted DCR method significantly improves reconstruction quality in real measurements and achieves image fidelity comparable to that of the measured system matrix reconstruction and superior to that of the model-based system matrix reconstruction. The proposed approximation of the spatially variant deconvolution preserves the reconstruction accuracy while reducing computational and memory requirements. Its computational complexity is reduced to 𝓞 (𝘕log𝘕). The method also successfully reconstructs simulated 3D data, demonstrating its applicability to volumetric MPI. Significance. The results demonstrate the potential of the adapted DCR approach to improve model-based MPI reconstruction while avoiding the need for a system matrix. The efficient deconvolution approximation makes the method particularly attractive for high-resolution and three-dimensional imaging by substantially improving its computational efficiency and scalability.Publicationtype: Journal Article7 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Parameter estimation for model-based sensing of magneto-mechanical resonatorsMagneto-mechanical resonators (MMRs) represent a recently proposed type of passive sensor that enables the estimation of its pose as well as sensing other parameters in its environment. The working principle of MMRs entails an excitation of the sensors by oscillating magnetic fields, followed by a readout process facilitated by inductive receiver coils. The sensing technology relies on real-time parameter estimation. This encompasses the solution of a nonlinear inverse problem, with the induced signals and a suitable forward model as inputs. The aim of this paper is twofold: first, to introduce a reference model and simplified models for the MMR dynamics and inductive readout, and second, to provide robust and real-time capable methods to estimate themodel parameters. The effectiveness of the presented methods is evaluated in terms of their real-time potential, precision, and accuracy. All presented methods demonstrate the capacity to estimate the measured signal, with the simplified methods reducing the corresponding parameter estimation time by up to two orders of magnitude at the expense of less than 4% deviation for large maximum deflection angles.Publicationtype: Journal Article21 11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Combining machine learning and physiological network models for sepsis prediction(Frontiers Media S.A., 2026-06-19); ; ; ; As the most extreme course of an infectious disease, sepsis poses a serious health threat, with a high mortality rate and frequent long-term consequences for survivors. Despite its enormous burden on global healthcare and ongoing research efforts, early sepsis onset prediction remains challenging due to the complex nature of its pathophysiology. Current approaches face a fundamental trade-off: data-driven machine learning models achieve strong performance but lack interpretability, while biologically inspired models provide mechanistic insights but have limited clinical validation. In this study, we propose the Latent Dynamics Model , a hybrid machine learning approach that integrates a functional model of coupled oscillators representing organ- and immune-cell populations and their interactions. Here, the model parameters encode physiological conditions and allow for an interpretable differentiation between healthy and pathological states. By projecting high-dimensional patient data into the low-dimensional parameter space of the functional model, machine-learned trajectories through this space allow the prediction of critical organ system states and simultaneously offer interpretability beyond plain risk estimates. The proposed method is trained and evaluated on real intensive care patients, achieving competitive AUROC/AUPRC performance on a retrospective MIMIC-IV cohort. Additional qualitative analysis reveals that the learned trajectories exhibit clinically plausible patterns of deterioration, recovery, and stability. We demonstrate that a physiological network model can be embedded within a deep learning architecture without compromising predictive performance while providing an interpretable latent structure for sepsis onset prediction.Publicationtype: Journal Article23 17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Genetically engineered bacterial magnetosomes as optimized tracers for magnetic particle imaging(Elsevier, 2026-05-29); ; ; ; Medical imaging relies on tracer materials to enable accurate visualization and diagnosis of diseases. Magnetic Particle Imaging (MPI) is an innovative tomographic modality that offers exceptional sensitivity and temporal resolution. These characteristics make MPI particularly promising for clinical applications such as real-time vascular and perfusion imaging, tumor detection, and intraoperative guidance. However, MPI performance has so far been limited by the quality of available tracers, as conventional chemical synthesis provides only restricted control over the size, shape, and magnetic properties of iron oxide nanoparticles. Biogenic magnetic nanoparticles, so-called magnetosomes, produced by magnetotactic bacteria, represent a compelling alternative. Magnetosome biosynthesis is fully genetically encoded, enabling the natural formation of magnetite nanoparticles with uniform size and morphology, which is difficult to achieve through chemical synthesis. Moreover, genetic engineering of the bacterial production host allows precise tuning of particle characteristics, including size, shape, and magnetic behavior, to meet specific application requirements. In this study, magnetosomes isolated from different Magnetospirillum gryphiswaldense mutant strains, each biomineralizing particles with distinct core diameters, were systematically evaluated as potential MPI tracers. Magnetic particle spectroscopy (MPS) was used to identify the most promising candidates based on their signal properties. These tracers were subsequently subjected to detailed signal analyses and phantom experiments to directly compare their imaging performance. Our findings demonstrate that genetically tailored magnetosomes can substantially improve MPI signal quality, underscoring their potential as next-generation tracers. This work provides a foundation for the rational design of optimized biogenic nanoparticles to advance preclinical and future clinical MPI applications. Statement of significance Magnetic Particle Imaging (MPI) is a novel imaging technology with high sensitivity and real-time capabilities, making it highly promising for clinical applications such as blood flow monitoring and tumor detection. The performance of MPI strongly depends on the properties of the tracer materials used. However, producing high-quality tracers through conventional chemical synthesis remains challenging. In this study, we introduce an innovative biological approach by using genetically engineered magnetotactic bacteria to produce uniform magnetic nanoparticles, so-called magnetosomes. This strategy allows precise control of particle size, shape, and magnetic properties, resulting in tracers with superior performance. Our findings pave the way for the development of next-generation MPI tracers, advancing both fundamental research and potential clinical translation.Publicationtype: Journal Article12 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Deep learning for restoring MPI system matrices using simulated training data(IOP Publishing, 2026-05-13); ; ; ; Objective. Magnetic particle imaging reconstructs tracer distributions using a system matrix (SM) obtained through time-consuming, noise-prone calibration measurements. Methods for addressing imperfections in measured system matrices increasingly rely on deep neural networks, yet curated training data remain scarce. This study evaluates whether physics-based simulated system matrices can be used to train deep learning (DL) models for different SM restoration tasks, i.e. denoising, accelerated calibration, upsampling, and inpainting, that generalize to measured data. Approach. A large dataset of system matrices was generated using an equilibrium magnetization model extended with uniaxial anisotropy. The dataset spans particle, scanner, and calibration parameters for 2D and 3D trajectories, and includes background noise injected from empty-frame measurements. For each restoration task, DL models were compared with classical non-learning baseline methods. Quantitative performance was evaluated on simulated data using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). For measured data, performance was assessed qualitatively by visual comparison of system matrices and the resulting reconstructions. Main results. The models trained solely on simulated system matrices generalized to measured data across all tasks: for denoising, DnCNN/RDN/SwinIR outperformed discrete cosine transform and soft thresholding baseline by dB PSNR and up to +0.1 SSIM on simulations and led to perceptually better reconstructions of real data; for 2D upsampling, SMRnet exceeded bicubic by ∼ 20 dB PSNR and ∼ 0.08 SSIM at ×2–×4 but these gains did not transfer qualitatively to real measurements. For 3D accelerated calibration, SMRnet matched tricubic in noiseless cases and was more robust under noise, and for 3D inpainting, biharmonic inpainting was superior when noise-free but degraded with noise, while a PConvUNet maintained quality and yielded less blurry reconstructions. Significance. The demonstrated transferability of DL models trained on simulations to real measurements mitigates the data-scarcity problem, which intensifies with model scale. This enables the development of new methods beyond current measurement capabilities and supports pre-training of large models on simulated data.Publicationtype: Journal Article38 48 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Optimizing the iterative reprojection phase retrieval algorithm Via Angle sampling(IEEE, 2026-05); ; ; ; Coherence-based imaging with synchrotron radiation offers spatial resolutions beyond the limits of conventional X-ray imaging and enables quantitative mapping of the refractive index. In nanotomography experiments with phase contrast, reconstruction is commonly performed in a two-step procedure, namely phase retrieval and computer tomography. One may enforce a 3D consistency constraint by repeatedly switching between both steps through a reprojection of the tomogram, which comes at a high computational cost. In this study, we accelerate the reprojection approach by updating the phase retrieval step on a subset of the projection angles. We show that the improvement is also reflected on the rest of angles through the tomographic reconstruction. We present an exemplary phantom to show that the angle sampling both accelerates convergence and helps reconstruction of challenging angles.Publicationtype: Conference Paper22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Comprehensive study of 3D liquid flow fields in additively manufactured structures for SMART reactors using large-scale vertical magnetic resonance imaging and computational fluid dynamics(Elsevier, 2026-04-24); ; ; ; Triply Periodic Minimal Surface (TPMS) structures have emerged as a new class of porous materials with variable geometries and favourable transport properties, making them promising for reactor internals in chemical engineering. However, experimental data on internal TPMS flow behaviour are still limited. To address this gap, the flow behaviour in additively manufactured TPMS structures is analysed using three-dimensional Magnetic Resonance Imaging (MRI) velocimetry in a large-bore vertical 3 T MRI system, in cylindrical columns of 38 mm diameter and Reynolds numbers between 50 and 300. Three different TPMS geometries are investigated, and consistency between Computational Fluid Dynamics (CFD) simulations and experimentally measured MRI velocity fields is established through cross-validation. The MRI system provides fully three-dimensional velocity fields with a divergence deviation below 6 %. MRI revealed distinct flow features: the Gyroid TPnS exhibited pronounced channelling, while the Schwarz-Diamond TPSf showed merge-split behaviour, achieving a 46 % increase in lateral mixing compared to the Gyroid TPnS structures. Numerical simulations reproduce the flow features and show agreement with the MRI data. The combined methodology demonstrates the suitability of MRI velocimetry for the experimental validation of CFD simulations and establishes a robust foundation for future studies of heat and mass transfer, as well as reactive flow, in structured reactor systems.Publicationtype: Journal Article75 90 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Variable gain amplifier for MPS(Infinite Science Publishing, 2026-04-23); ; ; The magnetic response of superparamagnetic iron oxide nanoparticles (SPIONs) varies significantly during nucle-ation and growth, requiring adaptive signal conditioning to avoid saturation and maintain sensitivity. This work presents the design and experimental characterization of a low-noise variable gain amplifier (VGA) based on the AD603 for this application. The VGA offers a pin-selectable gain range from −10 dB to +30 dB, enabling high gain at early synthesis stages and controlled attenuation at later stages. Performance was evaluated in terms of transient response, gain, phase behavior, and noise characteristics over a frequency range of 1 kHz to 1 MHz. Measurements demonstrate stable gain, limited phase variation, and improved noise performance through parallel amplifier configurations, indicating suitability for low-noise, high-dynamic-range SPIONs measurement systems such as MPS/MPI devices.Publicationtype: Journal Article17 10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Randomized greedy Kaczmarz for multi-contrast magnetic particle imaging(Infinite Science Publishing, 2026-03-18); ; ; ; Multi-contrast magnetic particle imaging allows for separating signals from different tracer materials or microenvi-ronments from one joint measurement, yielding multi-channel images that distinguish tracers or encode properties such as temperature or viscosity. However, leakage artifacts enforce a noticeably longer application of iterative standard methods like the Kaczmarz algorithm to reach satisfactory reconstruction results. A greedy randomized version of the Kaczmarz method is adapted, showing the potential for faster recovery.Publicationtype: Journal Article16 11