Real‐time diffraction computed tomography data reduction

Diffraction imaging is an X‐ray imaging method which uses the crystallinity information (cell parameter, orientation) as a signal to create an image pixel by pixel: a pencil beam is raster‐scanned onto a sample and the (powder) diffraction signal is recorded by a large area detector. With the flux p...

Full description

Saved in:
Bibliographic Details
Published in:Journal of synchrotron radiation 2018-03, Vol.25 (2), p.612-617
Main Authors: Kieffer, J., Petitdemange, S., Vincent, T.
Format: Article
Language:English
Subjects:
azimuthal integration
Computed tomography
Data analysis
Data reduction
Diffraction
diffraction imaging
FabIO
image compression
Imaging
Medical imaging
Physics
Pilatus detector
Pixels
powder diffraction
pyFAI
Raster scanning
Real time
silx
Synchrotrons
Citations: Items that this one cites
Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Diffraction imaging is an X‐ray imaging method which uses the crystallinity information (cell parameter, orientation) as a signal to create an image pixel by pixel: a pencil beam is raster‐scanned onto a sample and the (powder) diffraction signal is recorded by a large area detector. With the flux provided by third‐generation synchrotrons and the speed of hybrid pixel detectors, the acquisition speed of these experiments is now limited by the transfer rate to the local storage as the data reduction can hardly be performed in real time. This contribution presents the benchmarking of a typical data analysis pipeline for a diffraction imaging experiment like the ones performed at ESRF ID15a and proposes some disruptive techniques to decode CIF binary format images using the computational power of graphics cards to be able to perform data reduction in real time. This paper describes benchmarking of the data analysis pipeline for X‐ray diffraction computed tomography and CIF binary format byte‐offset decompression on a GPU.
ISSN:1600-5775
0909-0495
1600-5775
DOI:10.1107/S1600577518000607