Skip navigation
Skip navigation

Bayesian approach to time-resolved tomography

Myers, Glenn; Geleta, Matthew; Kingston, Andrew; Recur, Benoit; Sheppard, Adrian

Description

Conventional X-ray micro-computed tomography (μCT) is unable to meet the need for real-time, high-resolution, time-resolved imaging of multi-phase fluid flow. High signal-to-noise-ratio (SNR) data acquisition is too slow and results in motion artefacts in the images, while fast acquisition is too noisy and results in poor image contrast. We present a Bayesian framework for time-resolved tomography that uses priors to drastically reduce the required amount of experiment data. This enables...[Show more]

dc.contributor.authorMyers, Glenn
dc.contributor.authorGeleta, Matthew
dc.contributor.authorKingston, Andrew
dc.contributor.authorRecur, Benoit
dc.contributor.authorSheppard, Adrian
dc.date.accessioned2018-11-29T22:54:45Z
dc.date.available2018-11-29T22:54:45Z
dc.identifier.issn1094-4087
dc.identifier.urihttp://hdl.handle.net/1885/152908
dc.description.abstractConventional X-ray micro-computed tomography (μCT) is unable to meet the need for real-time, high-resolution, time-resolved imaging of multi-phase fluid flow. High signal-to-noise-ratio (SNR) data acquisition is too slow and results in motion artefacts in the images, while fast acquisition is too noisy and results in poor image contrast. We present a Bayesian framework for time-resolved tomography that uses priors to drastically reduce the required amount of experiment data. This enables high-quality time-resolved imaging through a data acquisition protocol that is both rapid and high SNR. Here we show that the framework: (i) encompasses our previous, algorithms for imaging two-phase flow as limiting cases; (ii) produces more accurate results from imperfect (i.e. real) data, where it can be compared to our previous work; and (iii) is generalisable to previously intractable systems, such as three-phase flow.
dc.format.mimetypeapplication/pdf
dc.publisherOptical Society of America
dc.sourceOptics Express
dc.titleBayesian approach to time-resolved tomography
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume23
dc.date.issued2015
local.identifier.absfor020402 - Condensed Matter Imaging
local.identifier.absfor040499 - Geophysics not elsewhere classified
local.identifier.absfor080106 - Image Processing
local.identifier.ariespublicationU3488905xPUB8563
local.type.statusPublished Version
local.contributor.affiliationMyers, Glenn, College of Science, ANU
local.contributor.affiliationGeleta, Matthew, College of Science, ANU
local.contributor.affiliationKingston, Andrew, College of Science, ANU
local.contributor.affiliationRecur, Benoit, College of Science, ANU
local.contributor.affiliationSheppard, Adrian, College of Science, ANU
local.bibliographicCitation.issue15
local.bibliographicCitation.startpage20062
local.bibliographicCitation.lastpage20074
local.identifier.doi10.1364/OE.23.020062
local.identifier.absseo970102 - Expanding Knowledge in the Physical Sciences
dc.date.updated2018-11-29T08:02:18Z
local.identifier.scopusID2-s2.0-84954468342
local.identifier.thomsonID000361035300144
dcterms.accessRightsOpen Access
CollectionsANU Research Publications

Download

File Description SizeFormat Image
01_Myers_Bayesian_approach_to_2015.pdf3.4 MBAdobe PDFThumbnail


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator