Compressive Sensing for Dynamic XRF Scanning

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.

Soft X-ray Fluorescence and Near-Edge Absorption Microscopy for Investigating Metabolic Features in Biological Systems: A Review

Scanning transmission X-ray microscopy (STXM) provides the imaging of biological specimens allowing the parallel collection of localized spectroscopic information by X-ray fluorescence (XRF) and/or X-ray Absorption Near Edge Spectroscopy (XANES). The complex metabolic mechanisms which can take place in biological systems can be explored by these techniques by tracing even small quantities of the chemical elements involved in the metabolic pathways. Here, we present a review of the most recent publications in the synchrotrons' scenario where soft X-ray spectro-microscopy has been employed in life science as well as in environmental research.

Dose-efficient multimodal microscopy of human tissue at a hard X-ray nanoprobe beamline

X-ray fluorescence microscopy performed at nanofocusing synchrotron beamlines produces quantitative elemental distribution maps at unprecedented resolution (down to a few tens of nanometres), at the expense of relatively long measuring times and high absorbed doses. In this work, a method was implemented in which fast low-dose in-line holography was used to produce quantitative electron density maps at the mesoscale prior to nanoscale X-ray fluorescence acquisition. These maps ensure more efficient fluorescence scans and the reduction of the total absorbed dose, often relevant for radiation-sensitive (e.g. biological) samples. This multimodal microscopy approach was demonstrated on human sural nerve tissue. The two imaging modes provide complementary information at a comparable resolution, ultimately limited by the focal spot size. The experimental setup presented allows the user to swap between them in a flexible and reproducible fashion, as well as to easily adapt the scanning parameters during an experiment to fine-tune resolution and field of view.

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.

Soft X-ray Microscopy Techniques for Medical and Biological Imaging at TwinMic—Elettra

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.

Gaming the beamlines—employing reinforcement learning to maximize scientific outcomes at large-scale user facilities

Beamline experiments at central facilities are increasingly demanding of remote, high-throughput, and adaptive operation conditions. To accommodate such needs, new approaches must be developed that enable on-the-fly decision making for data intensive challenges. Reinforcement learning (RL) is a domain of AI that holds the potential to enable autonomous operations in a feedback loop between beamline experiments and trained agents. Here, we outline the advanced data acquisition and control software of the Bluesky suite, and demonstrate its functionality with a canonical RL problem: cartpole. We then extend these methods to efficient use of beamline resources by using RL to develop an optimal measurement strategy for samples with different scattering characteristics. The RL agents converge on the empirically optimal policy when under-constrained with time. When resource limited, the agents outperform a naive or sequential measurement strategy, often by a factor of 100%. We interface these methods directly with the data storage and provenance technologies at the National Synchrotron Light Source II, thus demonstrating the potential for RL to increase the scientific output of beamlines, and layout the framework for how to achieve this impact.