Incomplete-data image reconstructions in industrial x-ray computerized tomography

In earlier works it was concluded that image reconstruction from incomplete data can be achieved through an iterative transform algorithm which utilizes the a priori information on the object to compensate for the missing data. The image is transformed back and forth between the object space and the projection space, being corrected by the a priori information on the object in the object space, and by the known projections in the projection space. The a priori information in the object space includes a boundary enclosing the object, and an upper bound and a lower bound of the object density. In this paper we report the results of testing the iterative transform algorithm on experimental data. X-ray sinogram data of the cross section of a F404 high-pressure turbine blade made of Ni-based superalloy were supplied to us by the Aircraft Engine Business Group of General Electric Company at Cincinnati, Ohio. From the data set we simulated two kinds of incomplete data situations, incomplete projection and limited-angle scanning, and applied the iterative transform algorithm to reconstruct the images. The results validated the practical value of the iterative transform algorithm in reconstructing images from incomplete x-ray data, both incomplete projections and limited-angle data. In all the cases tested there were significant improvements in the appearance of the images after iterations. The visual improvements are substantiated in a quantitative manner by the plots of errors in wall thickness measurements which in general decrease in magnitude with iterations