X-Ray Tomography Group
TOmographic Microscopy and Coherent rAdiology experimenTs
Prof. Stampanoni heads a group of over 20 people, including four beamline scientists, one industrial liaison scientist, two technicians, several scientists, many postdocs, PhD students, Master students, and Bachelor students. The team focuses on the development of tools, both instrumentation and algorithms, for tomographic X-ray imaging, exploiting synchrotron and laboratory sources. The group is engaged in the design and construction of ultra-fast data acquisition systems (stroboscopic coherent X-ray radiology and tomography) to provide dynamic investigation of rapidly evolving systems. The group also intensively develops optimized applications for fast, concurrent post-processing of tomographic data starting from simple normalization corrections to ad-hoc reconstruction and artifact reductions algorithms. Finally, the group investigates, creates and optimizes novel imaging modalities based on the coherent properties of synchrotron radiation and works on the translation of such work to conventional X-ray sources.
Employing high-resolution X-rays to diagnose breast cancer – PSI researchers nominated for the European Inventor Award.
Macroscopic mapping of microscale fibers in freeform injection molded fiber-reinforced composites using X-ray scattering tensor tomography
Prediction of the mechanical properties dictated by the local microfiber orientation is essential for the performance characterization of fiber-reinforced composites. Typically, tomographic imaging methods that provide fine spatial resolution are employed to investigate various materials' local micro- and nano-architecture in a non-destructive manner. However, conventional imaging techniques are limited by a substantial trade-off between the structure size of interest and the accessible field of view (FOV). Researchers from the TOMCAT beamline at Paul Scherrer Institut, Xnovo Technology ApS, and the Technical University of Denmark have demonstrated the potential of X-ray scattering tensor tomography for industrial applications by characterizing the microstructure of a centimeter-sized industrially relevant freeform injection molding fiber-reinforced composite sample. This emerging technique provides unprecedented access to microstructural information over centimeter-sized sample volumes paving the way towards its potential integration as an invaluable tool, for instance, in the fiber-reinforced-composite (FRC) industry. The obtained fiber orientation and anisotropy information over statistically relevant large volumes can be used to predict the mechanical properties of final products, optimize production parameters, and improve fiber injection molding simulation frameworks. The work is published in Composites Part B: Engineering on 15 March 2022.
Jisoo Kim was awarded the 2022 Werner Meyer-Ilse Memorial Award. The WMI Award is given to young scientists for exceptional contributions to the advancement of X-ray microscopy through either outstanding technical developments or applications, as evidenced by their presentation at the International Conference on X-ray Microscopy and supporting publications. Jisoo was awarded for his development of the method "Time-resolved x-ray scattering tomography for rheological studies", and is co-recipient of the award with Yanqi Luo from the Advanced Photons Source for her work on applications. The award was presented during the 15th International Conference on X-ray Microscopy XRM2022 hosted by the National Synchrotron Radiation Research Center (NSRRC) in Hsinchu, Taiwan on 19 - 24 June, 2022.
The orientation mismatch between the cone beam of an X-ray tube and the grating lines in a flat substrate remains a big challenge for laboratory grating-based X-ray interferometry, since it severely limits the imaging field of view. To solve this problem, we fabricated fan-shaped G0 source gratings by modulating the electric field during the deep reactive ion etching of silicon. With local electric field modulation in plasma we can etch high aspect ratio fan-shaped gratings that match the X-ray cone beam emission of a tube source. This new technology replaces the grating bending and allows a more compact design with larger field of view. Our work have recently been published in Applied Surface Science.
In the framework of the HERCULES European School about Neutrons & Synchrotron Radiation for Science which is coordinated by the Université de Grenoble Alpes and took place on February 28 – April 1, 2022, we were pleased to virtually host 4 students for a hands-on session. During this practical on “Absorption and phase contrast X-ray tomographic microscopy”, the students had the chance to go, with the help of a jupyter notebook and the guidance of our team members Margaux Schmeltz and Christian Schlepütz, through different examples of tomographic reconstructions. They learnt about basic decisions and tradeoffs that have to be taken into account when planning to acquire tomographic imaging data, were introduced to some segmentation tools and even got to play with dynamic tomographic data!
The X-ray Tomography group welcomes Marie-Christine Zdora as a new member. In her role as translational X-ray imaging adjunct scientist, Marie will mainly work on phase-contrast and dark-field imaging focusing on the further development of these techniques towards their clinical translation. Before joining TOMCAT, Marie was a postdoc in the X-ray optics and applications group at the Laboratory for Micro- and Nanotechnology (LMN) at PSI, where she worked on the development of new X-ray optics as well as X-ray wavefront sensing. Prior to this position, she was a research fellow at the University of Southampton in the UK, where she made advances in X-ray speckle-based imaging using synchrotron and lab sources.
Deep learning based classification of dynamic processes in time-resolved X-ray tomographic microscopy
Time-resolved X-ray tomographic microscopy provides new opportunities in the volumetric investigation of dynamic processes. Full exploitation of these new capabilities is currently still hindered by the lack of efficient post-processing approaches capable of handling TBs of noisy datasets. A deep learning based reconstruction and classification algorithm designed to reconstruct and segment dynamic processes within a static matrix with high efficiency is a solution to this issue. In a paper published recently in Scientific Reports, we demonstrate the advantages of the proposed approach on dynamic, time-resolved fuel cell data, for which the current data post-processing pipeline heavily relies on manual labor, typically limiting the experimental plans to just a small range of the full parameter space.
X-ray scattering tensor tomography facilitates the investigation of the microstructural organization in statistically large sample volumes. Established acquisition protocols based on scanning small-angle X-ray scattering and X-ray grating interferometry inherently require long scan times even with high brilliance X-ray sources. Recent developments in X-ray circular diffractive optics enable fast single-shot acquisition of the sample scattering properties with 2D omnidirectional sensitivity. Researchers from the TOMCAT beamline at Paul Scherrer Institut have proposed simple yet inherently rapid acquisition protocols for X-ray scattering tensor tomography leveraging these new optical elements. Results from both simulation and experimental data, supported by a null space analysis, suggest that the proposed acquisition protocols are rapid and corroborate, providing sufficient information for the accurate volumetric reconstruction of the scattering properties. The proposed acquisition protocols will be the cornerstones for rapid inspection or time-resolved tensor tomography of the microstructural organization over an extended field of view. The work is published in Scientific Reports on 29 November 2021.