Dual-energy imaging has emerged as a superior way to recognize materials in X-ray computed tomography. To estimate material properties such as effective atomic number and density, one often generates images in terms of basis functions. This requires decomposition of the dual-energy sinograms into basis sinograms, and subsequently reconstructing the basis images. However, the presence of metal can distort the reconstructed images. In this paper we investigate how photoelectric and Compton basis functions, and synthesized monochromatic basis (SMB) functions behave in the presence of metal and its effect on estimation of effective atomic number and density. Our results indicate that SMB functions, along with edge-preserving total variation regularization, show promise for improved material estimation in the presence of metal. The results are demonstrated using both simulated data as well as data collected from a dualenergy medical CT scanner.
Dual-energy computed tomography (CT) offers the potential to recognize material properties by decomposing sinograms into Compton and photoelectric bases and subsequently reconstructing the Compton and photoelectric images. However, the presence of high density materials such as metal can distort the reconstructed images, leading to inaccurate material characterization. In this paper, we present a reconstruction technique to reduce noise and metal artifacts in dual-energy CT images by exploiting (1) statistical correlation between measurements and decomposed sinograms, (2) intra-image correlation between decomposed images, and (3) inter-image sparsity. The algorithm is based on minimizing weighted least squares with edge-preserving total variation regularization and is solved using split-Bregman iterative techniques. Using experimental data acquired from a commercial scanner, we demonstrate that the proposed algorithm significantly reduces noise and metal artifacts compared to the baseline approaches of filtered back projection and competing iterative reconstructions algorithms.