When considering the acquisition of experimental synchrotron radiation (SR) X-ray CT data, the reconstruction workflow cannot be limited to the essential computational steps of flat fielding and filtered back projection (FBP). More refined image processing is often required, usually to compensate artifacts and enhance the quality of the reconstructed images. In principle, it would be desirable to optimize the reconstruction workflow at the facility during the experiment (beamtime). However, several practical factors affect the image reconstruction part of the experiment and users are likely to conclude the beamtime with sub-optimal reconstructed images. Through an example of application, this article presents SYRMEP Tomo Project (STP), an open-source software tool conceived to let users design custom CT reconstruction workflows. STP has been designed for post-beamtime (off-line use) and for a new reconstruction of past archived data at user’s home institution where simple computing resources are available. Releases of the software can be downloaded at the Elettra Scientific Computing group GitHub repository https://github.com/ElettraSciComp/STP-Gui.

Progress in nanotechnology calls for material probing techniques of high sensitivity and resolution. Such techniques are also used for high-impact studies of nanoscale materials in medicine and biology. Soft X-ray microscopy has been successfully used for investigating complex biological processes occurring at micrometric and sub-micrometric length scales and is one of the most powerful tools in medicine and the life sciences. Here, we present the capabilities of the TwinMic soft X-ray microscopy end-station at the Elettra synchrotron in the context of medical and biological imaging, while we also describe novel uses and developments.

Low-energy X-ray fluorescence (LEXRF) is an essential tool for bio-related research of organic samples, whose composition is dominated by light elements. Working at energies below 2 keV and being able to detect fluorescence photons of lightweight elements such as carbon (277 eV) is still a challenge, since it requires in-vacuum operations to avoid in-air photon absorption. Moreover, the detectors must have a thin entrance window and collect photons at an angle of incidence near 90 degrees to minimize the absorption by the protective coating. Considering the low fluorescence yield of light elements, it is important to cover a substantial part of the solid angle detecting ideally all emitted X-ray fluorescence (XRF) photons. Furthermore, the energy resolution of the detection system should be close to the Fano limit in order to discriminate elements whose XRF emission lines are often very close within the energy spectra. To ensure all these features, a system consisting of four monolithic multi-element silicon drift detectors was developed. The use of four separate detector units allows optimizing the incidence angle on all the sensor elements. The multi-element approach in turn provides a lower leakage current on each anode, which, in combination with ultra-low noise preamplifiers, is necessary to achieve an energy resolution close to the Fano limit. The potential of the new detection system and its applicability for typical LEXRF applications has been proved on the Elettra TwinMic beamline.

X-ray ptychography is an advanced computational microscopy technique, which is delivering exceptionally detailed quantitative imaging of biological and nanotechnology specimens, which can be used for high-precision X-ray measurements. However, coarse parametrisation in propagation distance, position errors and partial coherence frequently threaten the experimental viability. In this work, we formally introduce these actors, solving the whole reconstruction as an optimisation problem. A modern deep learning framework was used to autonomously correct the setup incoherences, thus improving the quality of a ptychography reconstruction. Automatic procedures are indeed crucial to reduce the time for a reliable analysis, which has a significant impact on all the fields that use this kind of microscopy. We implemented our algorithm in our software framework, SciComPty, releasing it as open-source. We tested our system on both synthetic datasets, as well as on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.