Advances on sparse Dynamic Scanning in Spectromicroscopy through Compressive Sensing

Abstract

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 resolution, improved sensitivities and higher acquisition speeds. The frontiers of those advances are on detectors, radiation sources, motors, but also on acquisition and analysis software together with general methodology improvements. We've e recently introduced and fully implemented on a soft X-ray microscopy beamline an intelligent scanning methodology based on compressive sensing. This demonstrated sparse low energy XRF scanning of dynamically chosen regions of interest in combination with STXM. It resulted in a sparse megapixel-rage low energy XRF spectroimaging through dynamic scanning that was previously not feasible. This research has been further developed and has been applied in scientific applications in biology. The developments are mostly on the dynamic triggering decisional mechanism in order to incorporate modern Machine Learning and real-time XRF fitting but also on 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 successfully used on different samples, from lung and ovarian human tissues to plant root sections. This manuscript introduces the latest advances of the method and demonstrates applications in life and environmental sciences. Finally it highlights an auxiliary development of a mobile application that assists the selection of specific regions of interest in an easy way.