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The possibility to obtain a three-dimensional representation of a single object with sub-$\mu$m resolution is crucial in many fields, from material science to clinical diagnostics. This is typically achieved through tomography, which combines multiple two-dimensional images of the same object captured at different orientations. However, this serial imaging method prevents single-shot acquisition in imaging experiments at free electron lasers. In the present experiment, we report on a new approach to 3D imaging using extreme-ultraviolet radiation. In this method, two EUV pulses hit simultaneously an isolated 3D object from different sides, generating independent coherent diffraction patterns, resulting in two distinct bidimensional views obtained via phase retrieval. These views are then used to obtain a 3D reconstruction using a ray tracing algorithm. This EUV stereoscopic imaging approach, similar to the natural process of binocular vision, provides sub-$\mu$m spatial resolution and single shot capability. Moreover, ultrafast time resolution and spectroscopy can be readily implemented, a further extension to X-ray wavelengths can be envisioned as well., Comment: Authors Danny Fainozzi and Matteo Ippoliti contributed equally

Computational methods are driving high impact microscopy techniques such as ptychography. However, the design and implementation of new algorithms is often a laborious process, as many parts of the code are written in close-to-the-hardware programming constructs to speed up the reconstruction. In this paper, we present SciComPty, a new ptychography software framework aiming at simulating ptychography datasets and testing state-of-the-art and new reconstruction algorithms. Despite its simplicity, the software leverages GPU accelerated processing through the PyTorch CUDA interface. This is essential to design new methods that can readily be employed. As an example, we present an improved position refinement method based on Adam and a new version of the rPIE algorithm, adapted for partial coherence setups. Results are shown on both synthetic and real datasets. The software is released as open-source.

X-ray Ptychography is an advanced computational microscopy technique which is delivering exceptionally detailed quantitative imaging of biological and nanotechnology specimens. However coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. In this work we formally introduced these actors, solving the whole reconstruction as an optimisation problem. A modern Deep Learning framework is used to correct autonomously 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 and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.

X-Ray Fluorescence (XRF) scanning is a widespread technique of high importance and impact since it provides chemical composition maps crucial for several scientific investigations. There are continuous requirements for larger, faster and highly resolved acquisitions in order to study complex structures. Among the scientific applications that benefit from it, some of them, such as wide scale brain imaging, are prohibitively difficult due to time constraints. However, typically the overall XRF imaging performance is improving through technological progress on XRF detectors and X-ray sources. This paper suggests an additional approach where XRF scanning is performed in a sparse way by skipping specific points or by varying dynamically acquisition time or other scan settings in a conditional manner. This paves the way for Compressive Sensing in XRF scans where data are acquired in a reduced manner allowing for challenging experiments, currently not feasible with the traditional scanning strategies. A series of different compressive sensing strategies for dynamic scans are presented here. A proof of principle experiment was performed at the TwinMic beamline of Elettra synchrotron. The outcome demonstrates the potential of Compressive Sensing for dynamic scans, suggesting its use in challenging scientific experiments while proposing a technical solution for beamline acquisition software., Comment: 16 pages, 7 figures, 1 table