title: Bayesian approach to time-resolved tomography.
abstract: 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.
authors.forename: Glenn R
authors.initials: GR
authors.lastname: Myers
authors.forename: Matthew
authors.initials: M
authors.lastname: Geleta
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Kingston
authors.forename: Benoit
authors.initials: B
authors.lastname: Recur
authors.forename: Adrian P
authors.initials: AP
authors.lastname: Sheppard