Bayesian approach to time-resolved tomography

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 no...

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Bibliographic Details
Published in:Optics express 2015-07, Vol.23 (15), p.20062-20074
Main Authors: Myers, Glenn R, Geleta, Matthew, Kingston, Andrew M, Recur, Benoit, Sheppard, Adrian P
Format: Article
Language:English
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Summary: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 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.
ISSN:1094-4087
1094-4087
DOI:10.1364/OE.23.020062