Abstract
X-ray computed tomography (CT) reconstruction with sparse projection views was proposed to reduce both the radiation dose and scan time. However, lacking of sufficient projection views may lead to severe artifacts for analytical reconstruction method such as the filtered back projection (FBP). Although the projection data is incomplete, we can generate the full-sampling system matrices according to the sufficient-sampling conditions [5]. Thus, we propose a novel iterative reconstruction model to fit the target images and the corresponding high resolution measurements in Radon domain by the full-sampling system matrices. Our proposed model is solved by the learned alternating minimization method, which accounts for a forward operator in deep neural network by the unrolling strategy. Numerical results demonstrate that the proposed approach outperforms some latest learning based reconstruction methods for the sparse-view CT problems.
The work is supported by NSFC 11701418, Major Science and Technology Project of Tianjin 18ZXRHSY00160 and Recruitment Program of Global Young Expert.
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Cheng, W., Wang, Y., Chi, Y., Xie, X., Duan, Y. (2019). Learned Full-Sampling Reconstruction. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11768. Springer, Cham. https://doi.org/10.1007/978-3-030-32254-0_42
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DOI: https://doi.org/10.1007/978-3-030-32254-0_42
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