Constrained TV-minimization image reconstruction for industrial CT system

In this work, we investigate the applicability of the constrained total-variation (TV)-minimization reconstruction method to industrial CT system. In general, industrial CT systems have the same principles of imaging process with clinical CT systems, but different imaging objectives and evaluation metrics. Optimization-based image reconstruction methods have been actively developed to meet practical challenges and extensively tested for clinical CT systems. However, the utility of optimization-based reconstruction methods is task-specific and not necessarily transferrable among different tasks. In this work, we adopt constrained TV-minimization programs together with adaptive-steepest-descent-projection-ontoconvex-sets (ASD-POCS) algorithm for reconstructing images from data of a concrete sample collected using a laboratory industrial CT system developed for non-destructive evaluation. Our results, compared to those reconstructed from FBPbased algorithm, suggest that the constrained TV-minimization program combined with ASD-POCS algorithm can yield images with comparable or improved visual quality and achieve equivalent or better imaging objectives over the currently used FBP-based algorithm under dense sampling data condition.