Reducing Metal Streak Artifacts in CT Images via Deep Learning: Pilot Results

Lars Gjesteby, Qingsong Yang, Yan Xi, Bernhard Claus, Yannan Jin, Bruno De Man, Ge Wang


Published in:Fully3D 2017 Proceedings


computed tomography (CT), deep learning, convolutional neural network (CNN), metal artifact reduction (MAR), proton therapy planning
Machine learning including deep learning is rapidly gaining popularity as a generic solution to problems across many fields. In medical imaging, deep learning has been successfully applied to image processing and analysis. In this paper, we employ a convolutional neural network (CNN) to address the long-standing problem of metal artifacts in CT images. Despite a vast number of metal artifact reduction (MAR) methods developed over the past four decades, there remain clinical areas in need of better results. Specifically, proton therapy planning requires high image quality for accurate tumor volume estimation. Errors in the image reconstruction may lead to treatment failure. In this paper, we merge deep learning with a state-of-the-art normalization-based MAR algorithm, NMAR, to correct metal streaks in critical image regions. Our results show that deep learning is a novel way to address CT reconstruction challenges, yielding images superior to the state of the art. 
Lars Gjesteby
Department of Biomedical Engineering, Rensselaer Polytechnic Institute
Yannan Jin
General Electric Global Research Center
Qingsong Yang
Department of Biomedical Engineering, Rensselaer Polytechnic Institute
Bruno De Man
General Electric Global Research Center
Yan Xi
First Imaging Technology
Bernhard Claus
General Electric Global Research Center
Ge Wang
Department of Biomedical Engineering, Rensselaer Polytechnic Institute
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