  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 time-consuming 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.
