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Improve angular resolution for sparse-view CT with residual convolutional neural network
Author(s): Kaichao Liang; Hongkai Yang; Kejun Kang; Yuxiang Xing
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Paper Abstract

Sparse-view CT imaging has been a hot topic in the medical imaging field. By decreasing the number of views, dose delivered to patients can be significantly reduced. However, sparse-view CT reconstruction is an illposed problem. Serious streaking artifacts occur if reconstructed with analytical reconstruction methods. To solve this problem, many researches have been carried out to optimize in the Bayesian framework based on compressed sensing, such as applying total variation (TV) constraint. However, TV or other regularized iterative reconstruction methods are time consuming due to iterative process needed. In this work, we proposed a method of angular resolution recovery in projection domain based on deep residual convolutional neural network (CNN) so that projections at unmeasured views can be estimated accurately. We validated our method by a disjointed data set new to trained networks. With recovered projections, reconstructed images have little streaking artifacts. Details corrupted due to sparse view are recovered. This deep learning based sinogram recovery can be generalized to more data insufficient situations.

Paper Details

Date Published: 9 March 2018
PDF: 11 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105731K (9 March 2018); doi: 10.1117/12.2293319
Show Author Affiliations
Kaichao Liang, Tsinghua Univ. (China)
Hongkai Yang, Tsinghua Univ. (China)
Kejun Kang, Tsinghua Univ. (China)
Yuxiang Xing, Tsinghua Univ. (China)

Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

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