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Proceedings Paper

Deep learning methods for CT image-domain metal artifact reduction
Author(s): Lars Gjesteby; Qingsong Yang; Yan Xi; Hongming Shan; Bernhard Claus; Yannan Jin; Bruno De Man; Ge Wang
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Paper Abstract

Artifacts resulting from metal objects have been a persistent problem in CT images over the last four decades. A common approach to overcome their effects is to replace corrupt projection data with values synthesized from an interpolation scheme or by reprojection of a prior image. State-of-the-art correction methods, such as the interpolation- and normalization-based algorithm NMAR, often do not produce clinically satisfactory results. Residual image artifacts remain in challenging cases and even new artifacts can be introduced by the interpolation scheme. Metal artifacts continue to be a major impediment, particularly in radiation and proton therapy planning as well as orthopedic imaging. A new solution to the long-standing metal artifact reduction (MAR) problem is deep learning, which has been successfully applied to medical image processing and analysis tasks. In this study, we combine a convolutional neural network (CNN) with the state-of-the-art NMAR algorithm to reduce metal streaks in critical image regions. Training data was synthesized from CT simulation scans of a phantom derived from real patient images. The CNN is able to map metal-corrupted images to artifact-free monoenergetic images to achieve additional correction on top of NMAR for improved image quality. Our results indicate that deep learning is a novel tool to address CT reconstruction challenges, and may enable more accurate tumor volume estimation for radiation therapy planning.

Paper Details

Date Published: 25 September 2017
PDF: 6 pages
Proc. SPIE 10391, Developments in X-Ray Tomography XI, 103910W (25 September 2017); doi: 10.1117/12.2274427
Show Author Affiliations
Lars Gjesteby, Rensselaer Polytechnic Institute (United States)
Qingsong Yang, Rensselaer Polytechnic Institute (United States)
Yan Xi, Rensselaer Polytechnic Institute (United States)
Hongming Shan, Rensselaer Polytechnic Institute (United States)
Bernhard Claus, GE Global Research Ctr. (United States)
Yannan Jin, GE Global Research Ctr. (United States)
Bruno De Man, GE Global Research Ctr. (United States)
Ge Wang, Rensselaer Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 10391:
Developments in X-Ray Tomography XI
Bert Müller; Ge Wang, Editor(s)

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