Advances in sparse dynamic scanning in spectromicroscopy through compressive sensing.

Scanning microscopies and spectroscopies like X-ray Fluorescence (XRF), Scanning Transmission X-ray Microscopy (STXM), and Ptychography are of very high scientific importance as they can be employed in several research fields. Methodology and technology advances aim at analysing larger samples at better resolutions, improved sensitivities and higher acquisition speeds. The frontiers of those advances are in detectors, radiation sources, motors, but also in acquisition and analysis software together with general methodology improvements. We have recently introduced and fully implemented an intelligent scanning methodology based on compressive sensing, on a soft X-ray microscopy beamline. This demonstrated sparse low energy XRF scanning of dynamically chosen regions of interest in combination with STXM, yielding spectroimaging data in the megapixel-range and in shorter timeframes than were previously not feasible. This research has been further developed and has been applied to scientific applications in biology. The developments are mostly in the dynamic triggering decisional mechanism in order to incorporate modern Machine Learning (ML) but also in the suitable integration of the method in the control system, making it available for other beamlines and imaging techniques. On the applications front, the method was previously successfully used on different samples, from lung and ovarian human tissues to plant root sections. This manuscript introduces the latest methodology advances and demonstrates their applications in life and environmental sciences. Lastly, it highlights the auxiliary development of a mobile application, designed to assist the user in the selection of specific regions of interest in an easy way.

Automatic Differentiation for Inverse Problems in X-ray Imaging and Microscopy.

Computational techniques allow breaking the limits of traditional imaging methods, such as time restrictions, resolution, and optics flaws. While simple computational methods can be enough for highly controlled microscope setups or just for previews, an increased level of complexity is instead required for advanced setups, acquisition modalities or where uncertainty is high; the need for complex computational methods clashes with rapid design and execution. In all these cases, Automatic Differentiation, one of the subtopics of Artificial Intelligence, may offer a functional solution, but only if a GPU implementation is available. In this paper, we show how a framework built to solve just one optimisation problem can be employed for many different X-ray imaging inverse problems.

Reconstruction of 3D topographic landscape in soft X-ray fluorescence microscopy through an inverse X-ray-tracing approach based on multiple detectors.

The study of X-ray fluorescence (XRF) emission spectra is a powerful technique used in applications that range from biology to cultural heritage. Key objectives of this technique include identification and quantification of elemental traces composing the analyzed sample. However, precise derivation of elemental concentration is often hampered by self-absorption of the XRF signal emitted by light constituents. This attenuation depends on the amount of sample present between the radiation source and detection system and allows for the exploitation of self-absorption in order to recover a sample topography. In this work, an X-ray-tracing application based on the use of multiple silicon drift detectors, is introduced to inversely reconstruct a 3D sample with correct topographical landscape, from 2D XRF count rates maps obtained from spectroscopy. The reconstruction was tested on the XRF maps of a simulated sample, which is composed of three cells with different size but similar composition. We propose to use the recovered 3D sample topography in order to numerically compute the self-absorption effects on the X-ray fluorescence radiation, thereby showing that a quantitative correction is possible. Lastly, we present a web application which implements the suggested methodology, in order to demonstrate its feasibility and applicability, available at: https://github.com/ElettraSciComp/xrfstir .

A modular software framework for the design and implementation of ptychography algorithms.

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 article, 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 for designing 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.

Megapixel scanning transmission soft X-ray microscopy imaging coupled with compressive sensing X-ray fluorescence for fast investigation of large biological tissues.

Soft X-ray microscopy coupled with low energy X-ray fluorescence is a powerful tool for investigating complex biological systems like cells and tissues. Due to certain characteristics of X-ray sources, sample stage motors, and detectors, the examination of large areas at high resolutions is very time consuming, often confining the analysis only to a restricted number of pre-selected representative regions. Here we propose and demonstrate a compressive sensing method that provides an alternative approach for overcoming such limitations and can be applied to different kinds of samples and other microscopy and analytical techniques.

Compressive Sensing for Dynamic XRF Scanning.

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.

Attosecond pulse shaping using a seeded free-electron laser.

Attosecond pulses are central to the investigation of valence- and core-electron dynamics on their natural timescales1-3. The reproducible generation and characterization of attosecond waveforms has been demonstrated so far only through the process of high-order harmonic generation4-7. Several methods for shaping attosecond waveforms have been proposed, including the use of metallic filters8,9, multilayer mirrors10 and manipulation of the driving field11. However, none of these approaches allows the flexible manipulation of the temporal characteristics of the attosecond waveforms, and they suffer from the low conversion efficiency of the high-order harmonic generation process. Free-electron lasers, by contrast, deliver femtosecond, extreme-ultraviolet and X-ray pulses with energies ranging from tens of microjoules to a few millijoules12,13. Recent experiments have shown that they can generate subfemtosecond spikes, but with temporal characteristics that change shot-to-shot14-16. Here we report reproducible generation of high-energy (microjoule level) attosecond waveforms using a seeded free-electron laser17. We demonstrate amplitude and phase manipulation of the harmonic components of an attosecond pulse train in combination with an approach for its temporal reconstruction. The results presented here open the way to performing attosecond time-resolved experiments with free-electron lasers.

Femtosecond covariance spectroscopy.

The success of nonlinear optics relies largely on pulse-to-pulse consistency. In contrast, covariance-based techniques used in photoionization electron spectroscopy and mass spectrometry have shown that a wealth of information can be extracted from noise that is lost when averaging multiple measurements. Here, we apply covariance-based detection to nonlinear optical spectroscopy, and show that noise in a femtosecond laser is not necessarily a liability to be mitigated, but can act as a unique and powerful asset. As a proof of principle we apply this approach to the process of stimulated Raman scattering in α-quartz. Our results demonstrate how nonlinear processes in the sample can encode correlations between the spectral components of ultrashort pulses with uncorrelated stochastic fluctuations. This in turn provides richer information compared with the standard nonlinear optics techniques that are based on averages over many repetitions with well-behaved laser pulses. These proof-of-principle results suggest that covariance-based nonlinear spectroscopy will improve the applicability of fs nonlinear spectroscopy in wavelength ranges where stable, transform-limited pulses are not available, such as X-ray free-electron lasers which naturally have spectrally noisy pulses ideally suited for this approach.

SYRMEP Tomo Project: a graphical user interface for customizing CT reconstruction workflows.

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.

Contribution of Ribonucleic Acid (RNA) to the Fourier Transform Infrared (FTIR) Spectrum of Eukaryotic Cells.

We report on an optimized protocol for the digestion of cellular RNA, which minimally affects the cell membrane integrity, maintaining substantially unaltered the vibrational contributions of the other cellular macromolecules. The design of this protocol allowed us to collect the first Fourier transform infrared (FTIR) spectra of intact hydrated B16 mouse melanoma cells deprived of RNA and to highlight the in-cell diagnostic spectral features of it. Complementing the cellular results with the FTIR analysis of extracted RNA, ds-DNA, ss-cDNA and isolated nuclei, we verified that the spectral component centered at ∼1220 cm-1 is a good qualitative and semiquantitative marker of cellular DNA, since it is minimally affected by cellular RNA removal. Conversely, the band centered at ∼1240 cm-1, conventionally attributed to RNA, is only a qualitative marker of it, since its intensity is majorly influenced by other macromolecules containing diverse phosphate groups, such as phospholipids and phosphorylated proteins. On the other hand, we proved that the spectral contribution centered at ∼1120 cm-1 is the most reliable indicator of variations in cellular RNA levels, that better correlates with cellular metabolic activity. The achievement of these results have been made possible also by the implementation of new methods for baseline correction and automated peak fitting, presented in this paper.