Structural Gaussian Priors for Bayesian CT reconstruction of Subsea Pipes

Abstract

A non-destructive testing (NDT) application of X-ray computed tomography (CT) is inspection of subsea pipes in operation via 2D cross-sectional scans. Data acquisition is timeconsuming and costly due to the challenging subsea environment. Reducing the number of projections in a scan can yield time and cost savings, but compromises the reconstruction quality, if conventional reconstruction methods are used. In this work we take a Bayesian approach to CT reconstruction and focus on designing an effective prior to make use of available structural information about the pipe geometry. We propose a new class of structural Gaussian priors to enforce expected material properties in different regions of the reconstructed image based on independent Gaussian priors in combination with global regularity through a Gaussian Markov Random Field (GMRF) prior. Numerical experiments with synthetic and real data show that the proposed structural Gaussian prior can reduce artifacts and enhance contrast in the reconstruction compared to using only a global GMRF prior or no prior at all. We show how the resulting posterior distribution can be efficiently sampled even for large-scale images, which is essential for practical NDT applications.