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Open Access Deep Learning Based Sinogram Correction for Metal Artifact Reduction

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Computed Tomography (CT) is a non-invasive imaging technique that reconstructs cross-sectional images of scenes from a series of projections acquired at different angles. In applications such as airport security luggage screening, the presence of dense metal clutter causes beam hardening and streaking in the resulting conventionally formed images. These artifacts can lead to object splitting and intensity shading that make subsequent labeling and identification inaccurate. Conventional approaches to metal artifact reduction (MAR) have post-processed the artifact-filled images or interpolated the metal regions of the sinogram projection data. In this work, we examine the use of deep-learning-based methods to directly correct the observed sinogram projection data prior to reconstruction using a fully convolutional network (FCN). In contrast to existing learning-based CT artifact reduction work, we work completely in the sinogram domain and train a network over the entire sinogram (versus just local image patches). Since the information in sinograms pertaining to objects is non-local, patch-based methods are not well matched to the nature of CT data. The use of an FCN provides better computational scaling than historical perceptron-based approaches. Using a poly-energetic CT simulation, we demonstrate the potential of this new approach in mitigating metal artifacts in CT.
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Keywords: DEEP LEARNING; METAL ARTIFACT; SINOGRAM

Document Type: Research Article

Publication date: January 1, 2018

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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.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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