Improving dynamic tomography, through Maximum a posteriori estimation

Glenn R. Myers; Matthew Geleta; Andrew M. Kingston; Benoit Recur; Adrian P. Sheppard

doi:10.1117/12.2061604

11 September 2014 Improving dynamic tomography, through Maximum a posteriori estimation

Glenn R. Myers, Matthew Geleta, Andrew M. Kingston, Benoit Recur, Adrian P. Sheppard

Proceedings Volume 9212, Developments in X-Ray Tomography IX; 921211 (2014) https://doi.org/10.1117/12.2061604
Event: SPIE Optical Engineering + Applications, 2014, San Diego, California, United States

Abstract

Direct study of pore-scale fluid displacements, and other dynamic (i.e. time-dependent) processes is not feasible with conventional X-ray micro computed tomography (μCT). We have previously verified that a priori knowledge of the underlying physics can be used to conduct high-resolution, time-resolved imaging of continuous, complex processes, at existing X-ray μCT facilities. In this paper we present a maximum a posteriori (MAP) model of the dynamic tomography problem, which allows us to easily adapt and generalise our previous dynamic μCT approach to systems with more complex underlying physics.

Citation Download Citation

Glenn R. Myers, Matthew Geleta, Andrew M. Kingston, Benoit Recur, and Adrian P. Sheppard "Improving dynamic tomography, through Maximum a posteriori estimation", Proc. SPIE 9212, Developments in X-Ray Tomography IX, 921211 (11 September 2014); https://doi.org/10.1117/12.2061604

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