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.
Kaichao Liang, Hongkai Yang, Kejun Kang, and Yuxiang Xing, "Improve angular resolution for sparse-view CT with residual convolutional neural network," Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105731K (Presented at SPIE Medical Imaging: February 14, 2018; Published: 9 March 2018); https://doi.org/10.1117/12.2293319.
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