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Deep learning methods to guide CT image reconstruction and reduce metal artifacts
Author(s): Lars Gjesteby; Qingsong Yang; Yan Xi; Ye Zhou; Junping Zhang; Ge Wang
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Paper Abstract

The rapidly-rising field of machine learning, including deep learning, has inspired applications across many disciplines. In medical imaging, deep learning has been primarily used for image processing and analysis. In this paper, we integrate a convolutional neural network (CNN) into the computed tomography (CT) image reconstruction process. Our first task is to monitor the quality of CT images during iterative reconstruction and decide when to stop the process according to an intelligent numerical observer instead of using a traditional stopping rule, such as a fixed error threshold or a maximum number of iterations. After training on ground truth images, the CNN was successful in guiding an iterative reconstruction process to yield high-quality images. Our second task is to improve a sinogram to correct for artifacts caused by metal objects. A large number of interpolation and normalization-based schemes were introduced for metal artifact reduction (MAR) over the past four decades. The NMAR algorithm is considered a state-of-the-art method, although residual errors often remain in the reconstructed images, especially in cases of multiple metal objects. Here we merge NMAR with deep learning in the projection domain to achieve additional correction in critical image regions. Our results indicate that deep learning can be a viable tool to address CT reconstruction challenges.

Paper Details

Date Published: 9 March 2017
PDF: 7 pages
Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101322W (9 March 2017); doi: 10.1117/12.2254091
Show Author Affiliations
Lars Gjesteby, Rensselaer Polytechnic Institute (United States)
Qingsong Yang, Rensselaer Polytechnic Institute (United States)
Yan Xi, Rensselaer Polytechnic Institute (United States)
Ye Zhou, Fudan Univ. (China)
Junping Zhang, Fudan Univ. (China)
Ge Wang, Rensselaer Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 10132:
Medical Imaging 2017: Physics of Medical Imaging
Thomas G. Flohr; Joseph Y. Lo; Taly Gilat Schmidt, Editor(s)

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