Andreas Balles

Multispectral computed tomography (CT) enables advanced material characterization by acquiring energy-resolved projection data. However, since the incoming X-ray flux is be distributed across multiple narrow energy bins, the photon count per bin is greatly reduced compared to standard energy-integrated imaging. This inevitably introduces substantial noise, which can either prolong acquisition times and make scan durations infeasible or degrade image quality with strong noise artifacts. To address this challenge, we present a dedicated neural network-based denoising approach tailored for multispectral CT projections acquired at the BM18 beamline of the ESRF. The method exploits redundancies across angular, spatial, and spectral domains through specialized sub-networks combined via stacked generalization and an attention mechanism. Non-local similarities in the angular-spatial domain are leveraged alongside correlations between adjacent energy bands in the spectral domain, enabling robust noise suppression while preserving fine structural details. Training was performed exclusively on simulated data replicating the physical and noise characteristics of the BM18 setup, with validation conducted on CT scans of custom-designed phantoms containing both high-Z and low-Z materials. The denoised projections and reconstructions demonstrate substantial improvements in image quality compared to classical denoising methods and baseline CNN models. Quantitative evaluations confirm that the proposed method achieves superior performance across a broad spectral range, generalizing effectively to real-world experimental data while significantly reducing noise without compromising structural fidelity.

Multispectral imaging is susceptible to high levels of noise due to the low photon flux observed, particularly in narrow energy bins. To address this issue, multispectral data was acquired at the BM18 beamline of the ESRF using a silicon prism array to refract a polychromatic beam into energy bins, which were then captured by an SCMOS detector. Unlike photon counting detectors, which suffer from pile-up and low energy resolution, the proposed method can handle the high flux of the unfiltered BM18 beam and split the beam into 100 energy bins. The resulting data, which was significantly affected by noise, was denoised using a neural network that had been trained on simulated data. The efficacy of the denoising method was evaluated on computed tomography scans of two phantoms containing high and low atomic number (Z) materials, as well as other samples. Despite differences between the simulation and experimental setups, the network demonstrated a significant reduction in noise, as illustrated in the projections and reconstructed slices. The denoised reconstructions exhibited a notable suppression of noise without compromising image detail or introducing additional artifacts, emphasizing the model's potential for enhancing multispectral imaging in material analysis.

X-ray full-field microscopy at laboratory sources for photon energies above 10 keV suffers from either long exposure times or low resolution. The photon flux is mainly limited by the objectives used, having a limited numerical aperture NA. We show that this can be overcome by making use of the cone-beam illumination of laboratory sources by imaging the same field of view (FoV) several times under slightly different angles using an array of X-ray lenses. Using this technique, the exposure time can be reduced drastically without any loss in terms of resolution. A proof-of-principle is given using an existing laboratory metal-jet source at the 9.25 keV Ga K_α-line and compared to a ray-tracing simulation of the setup.

In a comprehensive study, we demonstrate the performance and typical application scenarios for laboratory-based nano-computed tomography in materials research on various samples. Specifically, we focus on a projection magnification system with a nano focus source. The imaging resolution is quantified with common 2D test structures and validated in 3D applications by means of the Fourier Shell Correlation. As representative application examples from nowadays material research, we show metallization processes in multilayer integrated circuits, aging in lithium battery electrodes, and volumetric of metallic sub-micrometer fillers of composites. Thus, the laboratory system provides the unique possibility to image non-destructively structures in the range of 170–190 nanometers, even for high-density materials.

With increasing miniaturization in industry and medical technology, non-destructive testing techniques are an area of ever-increasing importance. In this framework, X-ray microscopy offers an efficient tool for the analysis, understanding, and quality assurance of microscopic samples, in particular as it allows reconstructing three-dimensional data sets of the whole sample’s volume via computed tomography (CT). The following article describes a compact X-ray microscope in the hard X-ray regime around 9 keV, based on a highly brilliant liquid-metal-jet source. In comparison to commercially available instruments, it is a hybrid that works in two different modes. The first one is a micro-CT mode without optics, which uses a high-resolution detector to allow scans of samples in the millimeter range with a resolution of 1 μm. The second mode is a microscope, which contains an X-ray optical element to magnify the sample and allows resolving 150 nm features. Changing between the modes is possible without moving the sample. Thus, the instrument represents an important step towards establishing high-resolution laboratory-based multi-mode X-ray microscopy as a standard investigation method.

We derive a propagator based formalism for optimizing phase contrast imaging in laboratory setups as well as in synchrotron setups. We confirm based on five different setups the well known existence of an optimum position for the sample in terms of phase contrast by measuring two types of fibers and evaluating the fringe contrast. Furthermore, we demonstrate for these setups a correlation of our formula and the fringe contrast. Hence, an estimate of this optimum position is given by our formalism which only depends on the source size, the detector blurring, and the total distance between source and detector.