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Advances in sparse dynamic scanning in spectromicroscopy through compressive sensing.

George Kourousias1, Fulvio Billè1, Francesco Guzzi1

  • 1Elettra-Sincrotrone Trieste, Basovizza, Trieste, Italy.

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This study introduces an intelligent scanning method using compressive sensing for advanced X-ray microscopy (XRF and STXM). This technique enables faster, high-resolution imaging of biological and environmental samples.

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Area of Science:

  • * Advanced microscopy and spectroscopy techniques.
  • * Applications in life and environmental sciences.

Background:

  • * Scanning microscopies like X-ray Fluorescence (XRF) and Scanning Transmission X-ray Microscopy (STXM) are crucial for various research fields.
  • * Technological advancements focus on analyzing larger samples with higher resolution, sensitivity, and speed.

Purpose of the Study:

  • * To introduce and detail an intelligent scanning methodology based on compressive sensing for soft X-ray microscopy.
  • * To demonstrate the application of this method in life and environmental sciences.
  • * To highlight advancements in Machine Learning integration and control system compatibility.

Main Methods:

  • * Implementation of an intelligent scanning methodology utilizing compressive sensing on a soft X-ray microscopy beamline.
  • * Integration of sparse low-energy XRF scanning with STXM for dynamic region-of-interest analysis.
  • * Development of a dynamic triggering mechanism incorporating Machine Learning (ML).

Main Results:

  • * Achieved spectroimaging data in the megapixel-range with significantly reduced acquisition times.
  • * Successfully applied the method to diverse samples, including human tissues and plant roots.
  • * Developed a mobile application to facilitate user selection of regions of interest.

Conclusions:

  • * The developed intelligent scanning methodology enhances the efficiency and capability of X-ray microscopy techniques.
  • * The integration of ML and improved control systems broadens the applicability of the method across various beamlines and imaging techniques.
  • * Latest advancements demonstrate significant potential for life and environmental science research.