arXiv subjects

Tobias Knopp

Publications and source records attributed to Tobias Knopp.

At least 19 recordsLinked to original sources

arXiv2026

The Tilting Mode: A New Degree of Freedom for Magneto-Mechanical Resonator Sensors

Magneto-mechanical resonators (MMRs) are an emerging class of passive, wireless sensors. Their torsional oscillation mode has recently been established for sensing and tracking applications. In this work, we report the identification and characterization of a second mechanical mode, the tilting mode, that provides sensitivity along an axis inaccessible to the torsional mode, opening up a new degree of freedom for tracking and sensing with a single MMR sensor. We derive an analytical model predicting the tilting frequency as a function of the geometric and magnetic parameters of the resonator, compare the tilting mode frequency to that of the torsional mode, and obtain a characteristic frequency ratio between the torsional and the tilting mode in the small angle approximation. Experimental characterization using three-axis excitation and detection confirms the mode's existence and its directional selectivity. Notably, the three-axis frequency response shows no observable cross-coupling between the torsional and the tilting mode. We further show that the tilting mode frequency follows the predicted dependence on magnet distance, confirming the analytical model and the mode's applicability for sensing, analogous to that of the torsional mode.

physics.app-ph↗

arXiv2026

Impact of Structural Design on Magneto-Mechanical Resonators: The Case of a Jewel Bearing Variant

Magneto-mechanical resonators (MMRs) are passive, wirelessly read sensors whose small size, miniaturizability, and low cost make them attractive for tracking and for sensing physical and chemical quantities. Their operation is based on a permanent-magnet rotor whose mechanical resonance encodes the sensing and tracking information. The bearing that suspends this rotor is therefore decisive both for the in-operation performance and for the manufacturability of the device. The original design suspends the rotor on a thin thread, which is nontrivial to assemble. This work investigates the impact of that structural design choice by introducing an alternative bearing in which the spherical rotor magnet rests in a cup-shaped industrial jewel. A dynamic model of this Jewel-MMR is derived, including the angle-dependent dry friction torque at the jewel contact. From it, a geometric trade-off between friction torque and resilience against unwanted oscillation modes is identified and quantified. Since the conventional quality factor loses its meaning under dominant dry friction, a two-tiered framework is proposed that compares MMR variants by estimation precision at equal magnet size and natural frequency. Both variants are characterized at a fixed pose on a Helmholtz-coil detection platform. The Jewel-MMR is self-aligning and assembled in less than half the time from fewer parts. Its response signal decays faster, so that under laboratory noise conditions the natural frequency and the orientation are recovered more precisely from the thread variant. The jewel bearing, however, tolerates a considerably larger deflection angle and thus a stronger signal, which reverses this relation above an experimentally determined noise level and indicates an advantage wherever the signal-to-noise ratio is reduced.

physics.app-ph↗

arXiv2026

Rapid quantitative chemical composition mapping using model-based MRI reconstruction with field inhomogeneity correction

Magnetic resonance spectroscopic imaging methods are particularly attractive for chemical engineering applications, including the monitoring of chemical reactions, where a rapid assessment of spatial variations in chemical composition is required. Conventional approaches, such as chemical shift imaging, introduce an additional spectral-encoding dimension, which substantially increases acquisition time. Consequently, fast spatially resolved spectroscopy remains an active research topic. This work uses a model-based reconstruction framework that embeds a priori spectral knowledge of the involved chemical components into the forward model to accelerate composition mapping. It allows for the reconstruction of molar ratio maps for individual chemical components without acquiring high-resolution spectra. Extending from previous studies, the proposed model accounts for inhomogeneities of the main field, which become more pronounced in systems with larger bores relevant for process engineering. Phantom experiments employing a 2D multi-gradient echo sequence demonstrate the ability to determine molar ratios for chemical components with single peaks as well as multiple peaks in their spectra. The bias and precision of the method remain around 0.01 mol/mol and 0.09 mol/mol, respectively, for a 20 s scan, indicating suitability for dynamic processes. Finally, acquisition time can be reduced further by applying sparse k-space sampling, potentially shortening the scan to 5 s with only minor degradation in quantitative performance.

eess.IV↗

arXiv2026

Deep Learning for Restoring MPI System Matrices Using Simulated Training Data

Magnetic particle imaging reconstructs tracer distributions using a system matrix 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 models for different system matrix restoration tasks, i.e., denoising, accelerated calibration, upsampling, and inpainting, that generalize to measured data. A large system matrices dataset 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, deep learning models were compared with classical non-learning baseline methods. The models trained solely on simulated system matrices generalized to measured data across all tasks: for denoising, DnCNN/RDN/SwinIR outperformed DCT-F baseline by >10 dB PSNR and up to 0.1 SSIM on simulations and led to perceptually better reconstuctions of real data; for 2D upsampling, SMRnet exceeded bicubic by 20 dB PSNR and 0.08 SSIM at $\times 2$-$\times 4$ which 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. The demonstrated transferability of deep learning models trained on simulations to real measurements mitigates the data-scarcity problem and enables the development of new methods beyond current measurement capabilities.

eess.IV↗

arXiv2026

Parameter Estimation for Model-Based Sensing of Magneto-Mechanical Resonators

Magneto-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 the model 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.

physics.app-ph↗

arXiv2026

Comprehensive Study of 3D Liquid Flow Fields in Additive Manufactured Structures for SMART Reactors Using Large-Scale Vertical Magnetic Resonance Imaging and Computational Fluid Dynamics

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.

physics.flu-dyn↗

arXiv2026

Automating Parameter Selection in Deep Image Prior for Fluorescence Microscopy Image Denoising via Similarity-Based Parameter Transfer

Unsupervised deep image prior (DIP) addresses shortcomings of training data requirements and limited generalization associated with supervised deep learning. The performance of DIP depends on the network architecture and the stopping point of its iterative process. Optimizing these parameters for a new image requires time, restricting DIP application in domains where many images need to be processed. Focusing on fluorescence microscopy data, we hypothesize that similar images share comparable optimal parameter configurations for DIP-based denoising, potentially enabling optimization-free DIP for fluorescence microscopy. We generated a calibration (n=110) and validation set (n=55) of semantically different images from an open-source dataset for a network architecture search targeted towards ideal U-net architectures and stopping points. The calibration set represented our transfer basis. The validation set enabled the assessment of which image similarity criterion yields the best results. We then implemented AUTO-DIP, a pipeline for automatic parameter transfer, and compared it to the originally published DIP configuration (baseline) and a state-of-the-art image-specific variational denoising approach. We show that a parameter transfer from the calibration dataset to a test image based on only image metadata similarity (e.g., microscope type, imaged specimen) leads to similar and better performance than a transfer based on quantitative image similarity measures. AUTO-DIP outperforms the baseline DIP (DIP with original DIP parameters) as well as the variational denoising approaches for several open-source test datasets of varying complexity, particularly for very noisy inputs. Applications to locally acquired fluorescence microscopy images further proved superiority of AUTO-DIP.

cs.CV↗

arXiv2025

Natural Frequency Dependency of Magneto-Mechanical Resonators on Magnet Distance

The precise derivation of physical quantities like temperature or pressure at arbitrary locations is useful in numerous contexts, e.g. medical procedures or industrial process engineering. The novel sensor technology of magneto-mechanical resonators (MMR), based on the interaction of a rotor and stator permanent magnet, allows for the combined tracking of the sensor position and orientation while simultaneously sensing an external measurand. Thereby, the quantity is coupled to the torsional oscillation frequency, e.g. by varying the magnet distance. In this paper, we analyze the (deflection angle-independent) natural frequency dependency of MMR sensors on the rotor-stator distance, and evaluate the performance of theoretical models. The three presented sensors incorporate magnets of spherical and/or cylindrical geometry and can be operated at adjustable frequencies within the range of 61.9-307.3 Hz. Our proposed method to obtain the natural frequency demonstrates notable robustness to variations in the initial deflection amplitudes and quality factors resulting in statistical errors on the mean smaller than 0.05 %. We find that the distance-frequency relationship is well described by an adapted dipole model accounting for material and manufacturing uncertainties. Their combined effect can be compensated by an adjustment of a single parameter which drives the median model deviation generally below 0.2 %. Our depicted methods and results are important for the design and calibration process of new sensor types utilizing the MMR technique.

physics.app-ph↗

arXiv2025

Simulating Gadolinium-Induced Magnetic Field Variations for Temperature Sensing with Magneto-Mechanical Resonators

Small-size magneto-mechanical resonators (MMR) represent an emerging class of passive, wireless sensors that combine a sensing functionality with a tracking option. The operation principle is based on a resonating rotor oscillation whose frequency is defined by the magnetic flux density of a stator magnet. One general sensing mechanism is the coupling of an external parameter to this resonator frequency. In this study, we investigate an approach for encoding a temperature information as a shift in the natural oscillation frequency utilizing the temperature-dependent magnetic properties of gadolinium (Gd). We perform an isolated simulation study on the temperature scaling of the magnetic field generation for stators coated with Gd of varying thickness. Our results show that the magnetic phase transition of Gd at its Curie temperature leads to a pronounced change in the magnetic permeability enabling a significant magnetic shielding behavior only for lower temperatures. In the transition regime, we find a peak sensitivity reaching 45.8 Hz/K exceeding existing values from the literature by up to a factor of $\sim$ 20. The findings of this work are an important step toward quantitative high-sensitivity temperature extraction with MMRs.

physics.app-ph↗

arXiv2025

Uncertainties of a Spherical Magnetic Field Camera

Spherical harmonic expansions are well-established tools for estimating magnetic fields from surface measurements and are widely used in applications such as tomographic imaging, geomagnetism, and biomagnetism. Although the mathematical foundations of these expansions are well understood, the impact of real-world imperfections, on the uncertainty of the field model has received little attention. In this work, we present a systematic uncertainty propagation analysis for a magnetic field camera that estimates the field from surface measurements using a spherical array of Hall magnetometers arranged in a spherical t-design. A Monte Carlo-based approach is employed to quantify how sensor-related uncertainties, such as calibration errors and positioning inaccuracies, affect the spatial distribution of the estimated field's uncertainty. The results offer insights into the robustness of spherical harmonic methods and help identify the dominant sources of uncertainty in practical implementations.

physics.ins-det↗

arXiv2025

Efficient Chebyshev Reconstruction for the Anisotropic Equilibrium Model in Magnetic Particle Imaging

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. A recently proposed direct reconstruction method leverages weighted Chebyshev polynomials in the frequency domain, removing the need for a simulated system matrix. However, the underlying model neglects key physical effects, such as nanoparticle anisotropy, leading to distortions in reconstructed images. To mitigate these artifacts, an adapted direct Chebyshev reconstruction (DCR) method incorporates a spatially variant deconvolution step, significantly improving reconstruction accuracy at the cost of increased computational demands. In this work, we evaluate the adapted DCR on six experimental phantoms, demonstrating enhanced reconstruction quality in real measurements and achieving image fidelity comparable to or exceeding that of simulated system matrix reconstruction. Furthermore, we introduce an efficient approximation for the spatially variable deconvolution, reducing both runtime and memory consumption while maintaining accuracy. This method achieves computational complexity of O(N log N ), making it particularly beneficial for high-resolution and three-dimensional imaging. Our results highlight the potential of the adapted DCR approach for improving model-based MPI reconstruction in practical applications.

physics.med-ph↗

arXiv2025

Real-Time 3D Magnetic Field Camera for a Spherical Volume

Accurate and efficient volumetric magnetic field measurements are essential for a wide range of applications. Conventional methods are often limited in terms of measurement speed and applicability, or suffer from scaling problems at larger volumes. This work presents the development of a magnetometer array designed to measure magnetic fields within a spherical volume at a frame rate of 10 Hz. The array consists of 3D Hall magnetometers positioned according to a spherical $t$-design, allowing simultaneous magnetic field data acquisition from the surface of the sphere. The approach enables the efficient representation of all three components of the magnetic field inside the sphere using a sixth-degree polynomial, significantly reducing measurement time compared to sequential methods. This work details the design, calibration, and measurement methods of the array. To evaluate its performance, we compare it to a sequential single-sensor measurement by examining a magnetic gradient field. The obtained measurement uncertainties of approx. 1% show the applicability for a variety of applications.

physics.app-ph↗

arXiv2025

Wireless and passive pressure detection using magneto-mechanical resonances in process engineering

A custom-developed magneto-mechanical resonator (MMR) for wireless pressure measurement is investigated for potential applications in process engineering. The MMR sensor utilises changes in the resonance frequency caused by pressure on a flexible 3D printed membrane. The thickness of the printed membrane plays a crucial role in determining the performance and sensitivity of MMRs, and can be tailored to meet the requirements of specific applications. The study includes static and dynamic measurements to determine the pressure sensitivity and temporal resolution of the sensor. The results show a minimum sensitivity of $0.06~\text{Hz mbar}^{-1}$ and are in agreement with theoretical calculations and measurements. The maximum sensor readout frequency is $2~\text{Hz}$ in this study. Additionally, the temperature dependence of the sensor is investigated, revealing a significant dependence of the resonance frequency on temperature. The developed MMR offers a promising and versatile method for precise pressure measurements in process engineering environments.

physics.app-ph↗

arXiv2025

Low-cost analog signal chain for transmit-receive circuits of passive induction-based resonators

Passive wireless sensors are crucial in modern medical and industrial settings to monitor procedures and conditions. We demonstrate a circuit to inductively excite passive resonators and to conduct their decaying signal response to a low noise amplifier. Two design variations of a generic transmit-receive signal chain are proposed, measured, and described in detail for the purpose of facilitating replication. Instrumentation and design aim to be scalable for multi-channel array configurations, using either off-the-shelf class-D audio amplifiers or a custom full H-bridge. Measurements are conducted on miniature magneto-mechanical resonators in the ultra low frequency range to enable sensing and tracking applications of such devices in different environments.

eess.SP↗

arXiv2025

Learned Discrepancy Reconstruction and Benchmark Dataset for Magnetic Particle Imaging

Magnetic Particle Imaging (MPI) is an emerging imaging modality based on the magnetic response of superparamagnetic iron oxide nanoparticles to achieve high-resolution and real-time imaging without harmful radiation. One key challenge in the MPI image reconstruction task arises from its underlying noise model, which does not fulfill the implicit Gaussian assumptions that are made when applying traditional reconstruction approaches. To address this challenge, we introduce the Learned Discrepancy Approach, a novel learning-based reconstruction method for inverse problems that includes a learned discrepancy function. It enhances traditional techniques by incorporating an invertible neural network to explicitly model problem-specific noise distributions. This approach does not rely on implicit Gaussian noise assumptions, making it especially suited to handle the sophisticated noise model in MPI and also applicable to other inverse problems. To further advance MPI reconstruction techniques, we introduce the MPI-MNIST dataset - a large collection of simulated MPI measurements derived from the MNIST dataset of handwritten digits. The dataset includes noise-perturbed measurements generated from state-of-the-art model-based system matrices and measurements of a preclinical MPI scanner device. This provides a realistic and flexible environment for algorithm testing. Validated against the MPI-MNIST dataset, our method demonstrates significant improvements in reconstruction quality in terms of structural similarity when compared to classical reconstruction techniques.

math.NA↗

arXiv2024

Equilibrium Model with Anisotropy for Model-Based Reconstruction in Magnetic Particle Imaging

Magnetic particle imaging is a tracer-based tomographic imaging technique that allows the concentration of magnetic nanoparticles to be determined with high spatio-temporal resolution. To reconstruct an image of the tracer concentration, the magnetization dynamics of the particles must be accurately modeled. A popular ensemble model is based on solving the Fokker-Plank equation, taking into account either Brownian or Néel dynamics. The disadvantage of this model is that it is computationally expensive due to an underlying stiff differential equation. A simplified model is the equilibrium model, which can be evaluated directly but in most relevant cases it suffers from a non-negligible modeling error. In the present work, we investigate an extended version of the equilibrium model that can account for particle anisotropy. We show that this model can be expressed as a series of Bessel functions, which can be truncated based on a predefined accuracy, leading to very short computation times, which are about three orders of magnitude lower than equivalent Fokker-Planck computation times. We investigate the accuracy of the model for 2D Lissajous magnetic particle imaging sequences and show that the difference between the Fokker-Planck and the equilibrium model with anisotropy is sufficiently small so that the latter model can be used for image reconstruction on experimental data with only marginal loss of image quality, even compared to a system matrix-based reconstruction.

eess.IV↗

arXiv2024

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 inductive coupling network is used. Our primary objectives are to achieve floating potentials to ensure patient safety, 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 of 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 to method-of-moment based simulations.

eess.SP↗

arXiv2024

Characterization of the Clinically Approved MRI Tracer Resotran for Magnetic Particle Imaging in a Comparison Study

Objective. The availability of magnetic nanoparticles with medical approval for human intervention is fundamental to the clinical translation of magnetic particle imaging (MPI). In this work, we thoroughly evaluate and compare the magnetic properties of an magnetic resonance imaging (MRI) approved tracer to validate its performance for MPI in future human trials. Approach. We analyze whether the recently approved MRI tracer Resotran is suitable for MPI. In addition, we compare Resotran with the previously approved and extensively studied tracer Resovist, with Ferrotran, which is currently in a clinical phase III study, and with the tailored MPI tracer Perimag. Main results. Initial magnetic particle spectroscopy measurements indicate that Resotran exhibits performance characteristics akin to Resovist, but below Perimag. We provide data on four different tracers using dynamic light scattering, transmission electron microscopy, vibrating sample magnetometry measurements, magnetic particle spectroscopy to derive hysteresis, point spread functions, and a serial dilution, as well as system matrix based MPI measurements on a preclinical scanner (Bruker 25/20 FF), including reconstructed images. Significance. Numerous approved magnetic nanoparticles used as tracers in MRI lack the necessary magnetic properties essential for robust signal generation in MPI. The process of obtaining medical approval for dedicated MPI tracers optimized for signal performance is an arduous and costly endeavor, often only justifiable for companies with a well-defined clinical business case. Resotran is an approved tracer that has become available in Europe for MRI. In this work, we study the eligibility of Resotran for MPI in an effort to pave the way for human MPI trials.

physics.med-ph↗