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Updated: Dec 17, 2025

Dynamic Pore-scale Reservoir-condition Imaging of Reaction in Carbonates Using Synchrotron Fast Tomography

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Published on: February 21, 2017

Compressive Sensing for Dynamic XRF Scanning.

George Kourousias¹, Fulvio Billè², Roberto Borghes²

¹Elettra - Sincrotrone Trieste S.C.p.A, 34149 Basovizza, Trieste, Italy. george.kourousias@elettra.eu.

Scientific Reports

|June 21, 2020

Summary

This summary is machine-generated.

Compressive Sensing (CS) offers a novel approach to X-Ray Fluorescence (XRF) scanning, enabling faster and more detailed chemical composition mapping. This technique allows for previously unfeasible complex structural studies, like large-scale brain imaging, by acquiring data more efficiently.

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Last Updated: Dec 17, 2025

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Published on: May 20, 2018

Area of Science:

Materials Science
Analytical Chemistry
Biomedical Imaging

Background:

X-Ray Fluorescence (XRF) scanning is vital for chemical composition mapping in scientific research.
Current XRF imaging faces limitations in speed and resolution for complex structures, hindering applications like large-scale brain imaging.
Advancements in XRF detectors and sources improve performance but do not fully address time constraints for demanding studies.

Purpose of the Study:

To introduce and evaluate Compressive Sensing (CS) strategies for sparse XRF scanning.
To enable challenging scientific experiments currently limited by traditional XRF acquisition methods.
To demonstrate the feasibility of CS for dynamic XRF scans.

Main Methods:

Implementing sparse XRF scanning by selectively skipping acquisition points.
Dynamically varying acquisition times and scan settings based on conditional criteria.
Developing and testing various CS strategies for dynamic XRF data acquisition.
Conducting a proof-of-principle experiment at the TwinMic beamline, Elettra synchrotron.

Main Results:

Demonstrated the potential of Compressive Sensing for dynamic XRF scans.
Showcased the ability of CS to acquire data in a reduced manner, overcoming time constraints.
Validated the effectiveness of CS strategies in a synchrotron environment.

Conclusions:

Compressive Sensing presents a viable approach to enhance XRF scanning capabilities.
This method facilitates complex experiments, such as large-scale brain imaging, previously deemed unfeasible.
The study proposes a technical solution for integrating CS into beamline acquisition software for broader adoption.