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Bayesian approach to time-resolved tomography
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DOI：10.1364/OE.23.020062.png)
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摘要
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Conventional X-ray micro-computed tomography (mu CT) 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. (C) 2015 Optical Society of America
Keyword:
PROJECTION DATA
RECONSTRUCTION
IMAGES
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期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
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