arXiv subjects

Imke Greving

Publications and source records attributed to Imke Greving.

9 recordsLinked to original sources

arXiv2026

Differentiable Jitter Correction using Deep Learning-based Image Quality Metric for Phase-Contrast Micro-CT

This paper proposes a fully differentiable jitter correction method for X-ray phase-contrast micro computed tomography using a deep learning-based image quality metric that estimates and compensates per-projection rigid jitter directly from the acquired projection data, without a pre-scan motion-free reference. The approach builds on a gradient-based auto-focus strategy adapted to parallel-beam geometry. A set of candidate objective functions is benchmarked in a controlled study, and the sensitivity of the visual information fidelity (VIF) metric to the jitter artifact is verified with the target phase-contrast data. To operate without a clean reference, a compact 3D convolutional neural network is trained to predict the VIF score from a single corrupted volume. A spatially selective total variation penalty applied exclusively to the image background is introduced to penalize spurious high-frequency structures that otherwise emerge during optimization. Experiments on biological specimens acquired at different synchrotron beamlines are conducted. Evaluation uses jitter motion applied to simulated and experimentally acquired projection data. The result confirms that the integrated pipeline reliably recovers fine structural detail lost due to jitter, with generalization demonstrated across morphologically distinct samples.

cs.CV↗

arXiv2026

Fast Ptychographic Near-Field Computed Tomography

Near-field X-ray ptychography enables quantitative three-dimensional imaging with nanometer-scale resolution, but its broader application is limited by long acquisition times - often necessitating cryogenic or vacuum environments - and demanding sample preparation procedures. In this paper, we present a flexible near-field ptychography setup that operates under ambient conditions and features an adjustable geometry for different sample sizes and resolution requirements. By replacing stepwise angular acquisition with a fly-rotation sample motion, the scanning overhead is reduced by a factor of 11, drastically lowering the tomographic scan time. To address the bottleneck in sample preparation, we also introduce a sample milling machine that enables fast preparation of specimen pillars with diameters down to 20 um. By lowering both preparation effort and measurement time while maintaining high spatial resolution, the presented system overcomes key limitations of near-field ptychography and makes quantitative X-ray nanotomography substantially more accessible for biological research.

physics.optics↗

arXiv2025

Self-supervised physics-informed generative networks for phase retrieval from a single X-ray hologram

X-ray phase contrast imaging significantly improves the visualization of structures with weak or uniform absorption, broadening its applications across a wide range of scientific disciplines. Propagation-based phase contrast is particularly suitable for time- or dose-critical in vivo/in situ/operando (tomography) experiments because it requires only a single intensity measurement. However, the phase information of the wave field is lost during the measurement and must be recovered. Conventional algebraic and iterative methods often rely on specific approximations or boundary conditions that may not be met by many samples or experimental setups. In addition, they require manual tuning of reconstruction parameters by experts, making them less adaptable for complex or variable conditions. Here we present a self-learning approach for solving the inverse problem of phase retrieval in the near-field regime of Fresnel theory using a single intensity measurement (hologram). A physics-informed generative adversarial network is employed to reconstruct both the phase and absorbance of the unpropagated wave field in the sample plane from a single hologram. Unlike most deep learning approaches for phase retrieval, our approach does not require paired, unpaired, or simulated training data. This significantly broadens the applicability of our approach, as acquiring or generating suitable training data remains a major challenge due to the wide variability in sample types and experimental configurations. The algorithm demonstrates robust and consistent performance across diverse imaging conditions and sample types, delivering quantitative, high-quality reconstructions for both simulated data and experimental datasets acquired at beamline P05 at PETRA III (DESY, Hamburg), operated by Helmholtz-Zentrum Hereon. Furthermore, it enables the simultaneous retrieval of both phase and absorption information.

physics.optics↗

arXiv2025

Directional Dark Field for Nanoscale Full-Field Transmission X-Ray Microscopy

Dark-field X-ray imaging visualizes structural inhomogeneities through small-angle scattering, but existing directional methods are confined to the micrometer scale. While recent advances have extended dark-field capabilities to nanoscale transmission X-ray microscopy, directional scattering retrieval - critical for characterizing anisotropic nanostructures - has remained inaccessible for imaging resolutions in the sub-micrometer scale. Here, we demonstrate the first directional dark-field setup for nanoimaging, achieving orientation mapping of scattering features below the spatial resolution limit. Our method is experimentally simple to implement with existing transmission X-ray microscopy setups. We validate its performance by successfully resolving sub-resolution test structure orientations, cross-correlating orientational changes within hierarchical nanoporous materials, and mapping the directional arrangement of hydroxyapatite nanocrystals 30 - 70 nm within human tooth enamel. By utilizing shadow regions in the optical configuration, we further extend the detectable scattering vector range, demonstrating a pathway toward size-selective dark-field imaging. This advancement enables the quantitative structural characterization of anisotropic nanomaterials, which are critical to biomineralization, advanced materials, and nanotechnology applications.

physics.optics↗

arXiv2025

ContextLoss: Context Information for Topology-Preserving Segmentation

In image segmentation, preserving the topology of segmented structures like vessels, membranes, or roads is crucial. For instance, topological errors on road networks can significantly impact navigation. Recently proposed solutions are loss functions based on critical pixel masks that consider the whole skeleton of the segmented structures in the critical pixel mask. We propose the novel loss function ContextLoss (CLoss) that improves topological correctness by considering topological errors with their whole context in the critical pixel mask. The additional context improves the network focus on the topological errors. Further, we propose two intuitive metrics to verify improved connectivity due to a closing of missed connections. We benchmark our proposed CLoss on three public datasets (2D & 3D) and our own 3D nano-imaging dataset of bone cement lines. Training with our proposed CLoss increases performance on topology-aware metrics and repairs up to 44% more missed connections than other state-of-the-art methods. We make the code publicly available.

cs.CV↗

arXiv2025

3D evolution of protein networks and lipid globules in heat-treated egg yolk

Upon heating, egg yolk transforms from a liquid to a gel due to protein denaturation. This process can serve as a useful model to better understand protein denaturation in general. Using x-ray holographic tomography, we investigated the structural changes in egg yolk during boiling without the need for complex sample fixation or drying. Our results reveal a developing separation between proteins and lipids, with fatty components rapidly aggregating into large globules that subsequently evolve into bubbles.

cond-mat.soft↗

arXiv2025

An ontology-based description of nano computed tomography measurements in electronic laboratory notebooks: from metadata schema to first user experience

In recent years, the importance of well-documented metadata has been discussed increasingly in many research fields. Making all metadata generated during scientific research available in a findable, accessible, interoperable, and reusable (FAIR) manner remains a significant challenge for researchers across fields. Scientific communities are agreeing to achieve this by making all data available in a semantically annotated knowledge graph using semantic web technologies. Most current approaches do not gather metadata in a consistent and community-agreed standardized way, and there are insufficient tools to support the process of turning them into a knowledge graph. We present an example solution in which the creation of a schema and ontology are placed at the beginning of the scientific process which is then - using the electronic laboratory notebook framework Herbie - turned into a bespoke data collection platform to facilitate validation and semantic annotation of the metadata immediately during an experiment. Using the example of synchrotron radiation-based nano computed tomography measurements, we present a holistic approach which can capture the complex metadata of such research instruments in a flexible and straightforward manner. Different instrument setups of this beamline can be considered, allowing a user-friendly experience. We show how Herbie turns all semantic documents into an accessible user interface, where all data entered automatically fulfills all requirements of being FAIR, and present how data can be directly extracted via competency questions without requiring familiarity with the fine-grained structure of the knowledge graph.

cs.DB↗

arXiv2024

Nanoscale Dark-Field Imaging in Full-Field Transmission X-Ray Microscopy

The dark-field signal uncovers details beyond conventional X-ray attenuation contrast, which is especially valuable for material sciences. In particular, dark-field techniques are able to reveal structures beyond the spatial resolution of a setup. However, its implementation is yet limited to the micrometer regime. Therefore, we propose a technique to extend full-field transmission X-ray microscopy by the dark-field signal. The proposed method is based on a well-defined illumination of a beam-shaping condenser, which allows to block the bright-field by motorized apertures in the back focal plane of the objective's lens. This method offers a simple implementation and enables rapid modality changes while maintaining short scan times, making dark-field imaging widely available at the nanometer scale.

physics.optics↗

arXiv2022

Wafer-Scale Fabrication of Hierarchically Porous Silicon and Silica Glass by Active Nanoparticle-Assisted Chemical Etching and Pseudomorphic Thermal Oxidation

Many biological materials exhibit a multiscale porosity with small, mostly nanoscale pores as well as large, macroscopic capillaries to simultaneously achieve optimized mass transport capabilities and lightweight structures with large inner surfaces. Realizing such a hierarchical porosity in artificial materials necessitates often sophisticated and expensive top-down processing that limits scalability. Here we present an approach that combines self-organized porosity based on metal-assisted chemical etching (MACE) with photolithographically induced macroporosity for the synthesis of single-crystalline silicon with a bimodal pore-size distribution, i.e., hexagonally arranged cylindrical macropores with 1 micrometer diameter separated by walls that are traversed by mesopores 60 nm across. The MACE process is mainly guided by a metal-catalyzed reduction-oxidation reaction, where silver nanoparticles (AgNPs) serve as the catalyst. In this process, the AgNPs act as self-propelled particles that are constantly removing silicon along their trajectories. High-resolution X-ray imaging and electron tomography reveal a resulting large open porosity and inner surface for potential applications in high-performance energy storage, harvesting and conversion or for on-chip sensorics and actuorics. Finally, the hierarchically porous silicon membranes can be transformed structure-conserving by thermal oxidation into hierarchically porous amorphous silica, a material that could be of particular interest for opto-fluidic and (bio-)photonic applications due to its multiscale artificial vascularization.

cond-mat.mtrl-sci↗