AI3SD Project: Artificial intelligence for reconstruction and super-resolution of chemical tomography
AI3SD Project: Artificial intelligence for reconstruction and super-resolution of chemical tomography
X-ray scatter-based tomography allows unprecedented insight into the chemical and physical state of functional materials and devices. Such tomographies can be used as research tools but also offer the prospect of routine scanning for security and inspection systems and potential for medical scanning. However X-ray scatter tomogrpahy requires longer collection times and higher doses than conventional absorption tomography - in this project we will develop machine learning tools to fuse X-ray scatter and X-ray absorption tomogrpahy, providing the detail ofthe former with the efficiency of the latter.
In conventional X-ray tomography, the images that are obtained give maps of density withinthe object and the composing pixels contain single grey scale values. In scatter based tomography, each pixel instead contains spectrum or equivalent chemical signal i.e. a 1D array (or higher) of numbers. An X-ray scatter tomography slice becomes a data cube with the two conventional spatial dimensions and a third spectral dimension. Such image data is termed
hyperspectral.
AI3SD, Funded Project, Artifical Intelligence, Chemical Tomography
University of Southampton
Butler, Keith Tobias
f28d27ee-757c-4ddd-be9e-6c930cdb42bb
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
21 April 2021
Butler, Keith Tobias
f28d27ee-757c-4ddd-be9e-6c930cdb42bb
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Butler, Keith Tobias
,
Kanza, Samantha and Frey, Jeremy G.
(eds.)
(2021)
AI3SD Project: Artificial intelligence for reconstruction and super-resolution of chemical tomography
(AI3SD-Project-Series, 4)
University of Southampton
9pp.
(doi:10.5258/SOTON/P0037).
Record type:
Monograph
(Project Report)
Abstract
X-ray scatter-based tomography allows unprecedented insight into the chemical and physical state of functional materials and devices. Such tomographies can be used as research tools but also offer the prospect of routine scanning for security and inspection systems and potential for medical scanning. However X-ray scatter tomogrpahy requires longer collection times and higher doses than conventional absorption tomography - in this project we will develop machine learning tools to fuse X-ray scatter and X-ray absorption tomogrpahy, providing the detail ofthe former with the efficiency of the latter.
In conventional X-ray tomography, the images that are obtained give maps of density withinthe object and the composing pixels contain single grey scale values. In scatter based tomography, each pixel instead contains spectrum or equivalent chemical signal i.e. a 1D array (or higher) of numbers. An X-ray scatter tomography slice becomes a data cube with the two conventional spatial dimensions and a third spectral dimension. Such image data is termed
hyperspectral.
Text
AI3SD-Project-Series_Report_4_Butler_FinalReport
- Version of Record
Text
AI3SD-Project-Series_Report_4_Butler_InterimReport
- Other
More information
Published date: 21 April 2021
Keywords:
AI3SD, Funded Project, Artifical Intelligence, Chemical Tomography
Identifiers
Local EPrints ID: 450087
URI: http://eprints.soton.ac.uk/id/eprint/450087
PURE UUID: bc0d5132-74c5-4bcc-b2fd-1af325de334c
Catalogue record
Date deposited: 09 Jul 2021 16:33
Last modified: 17 Mar 2024 03:51
Export record
Altmetrics
Contributors
Author:
Keith Tobias Butler
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
