Fusion of X-ray and geometrical data in computed tomography for nondestructive testing applications

X-ray computed tomography (CT) is widely used in nondestructive testing (NDT) techniques. While in medical imaging, classical methods based on backprojection (BP) or algebraic reconstruction techniques (ART) are satisfactory, in NDT applications, data acquisition constraints are such that these methods do not give satisfactory results. There is then a need for extra information and other kinds of data. In this paper, we consider an X-ray CT image reconstruction problem using two different kind of data: classical X-ray radiographic data and geometrical information and propose new methods based on regularization and Bayesian estimation for this data fusion problem. We use two kinds of geometrical information: partial knowledge of values in some regions and partial knowledge of the edges of other regions. We show the advantages of using such information on increasing the quality of reconstructions in a NDT application of wide layered shape (sandwich) structures. We also show results to analyze the effects of errors in these data on the reconstruction results.