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Open Access Edge-preserving total variation regularization for dual-energy CT images

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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.
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Keywords: Dual-energy CT image; Edge-preserving total variation; Metal artifact reduction; Split-Bregman optimization method

Document Type: Research Article

Publication date: January 13, 2019

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

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