Compressive Sensing for Dynamic XRF Scanning

Autor: Roberto Alberti, Fulvio Billè, Alessandra Gianoncelli, Roberto Borghes, George Kourousias, Simone Sala, Antonio Alborini
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Physics - Instrumentation and Detectors
Computer science
Real-time computing
FOS: Physical sciences
lcsh:Medicine
02 engineering and technology
Imaging techniques
01 natural sciences
Article
law.invention
Neuroimaging
law
FOS: Electrical engineering
electronic engineering
information engineering
Information theory and computation
lcsh:Science
94-XX
Microscopy
Multidisciplinary
Image and Video Processing (eess.IV)
010401 analytical chemistry
Detector
lcsh:R
Scientific data
Instrumentation and Detectors (physics.ins-det)
Electrical Engineering and Systems Science - Image and Video Processing
021001 nanoscience & nanotechnology
Fluorescence
Synchrotron
0104 chemical sciences
Compressed sensing
Beamline
Physics - Data Analysis
Statistics and Probability
lcsh:Q
0210 nano-technology
Data Analysis
Statistics and Probability (physics.data-an)
Zdroj: Scientific Reports, Vol 10, Iss 1, Pp 1-8 (2020)
Scientific Reports
ISSN: 2045-2322
DOI: 10.1038/s41598-020-66435-6
Popis: 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.
Comment: 16 pages, 7 figures, 1 table
Databáze: OpenAIRE
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