Conventional X-ray computed tomography (CT) systems obtain single- or dual-energy measurements, from which dual-energy CT has emerged as the superior way to recognize materials. Recently photon counting detectors have facilitated multi-spectral CT which captures spectral information by counting photon arrivals at different energy windows. However, the narrow energy bins result in a lower signal-to-noise ratio in each bin, particularly in the lower energy bins. This effect is significant and challenging when high-attenuation materials such as metal are present in the area to be imaged. In this paper, we propose a novel technique to estimate material properties with multi-spectral CT in the presence of high-attenuation materials. Our approach combines basis decomposition concepts using multiple-spectral bin information, as well as individual energy bin reconstructions. We show that this approach is robust in the presence of metal and outperforms alternative techniques for material estimation with multi-spectral CT as well with the state-of-art dual-energy CT.
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.