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1.	Qian, Y., et al. (author) Sparse-View CT Reconstruction Based on Improved Re-Sidual Network 2020 In: Computational and Experimental Simulations in Engineering. - Cham : Springer. ; , s. 1069-1080 Book chapter (peer-reviewed)abstract With the development of CT imaging, people have higher requirements for the quality of CT image reconstruction. It is desirable to use as low as reasonably achievable X-ray dose while meeting the quality of imaging requirements. Sparse-view reconstruction is a valid measure to resolve the radiation dose problem. Owing to the angular range of projection data does not satisfy the data completeness condition, sparse-view reconstruction has always been a conundrum in CT image reconstruction. In this paper, we introduces a new CT sparse-view reconstruction algorithm, which bases on the residual network. We optimize traditional residual models by improving the superfluous modules and reducing unnecessary calculations. Compared to several other classic methods, the experimental results with our network obtained better consequent, regarding artifact reduction, feature preservation, and computational speed.

Qian, Y., et al. (author)
Sparse-View CT Reconstruction Based on Improved Re-Sidual Network
2020
In: Computational and Experimental Simulations in Engineering. - Cham : Springer. ; , s. 1069-1080
Book chapter (peer-reviewed)abstract
- With the development of CT imaging, people have higher requirements for the quality of CT image reconstruction. It is desirable to use as low as reasonably achievable X-ray dose while meeting the quality of imaging requirements. Sparse-view reconstruction is a valid measure to resolve the radiation dose problem. Owing to the angular range of projection data does not satisfy the data completeness condition, sparse-view reconstruction has always been a conundrum in CT image reconstruction. In this paper, we introduces a new CT sparse-view reconstruction algorithm, which bases on the residual network. We optimize traditional residual models by improving the superfluous modules and reducing unnecessary calculations. Compared to several other classic methods, the experimental results with our network obtained better consequent, regarding artifact reduction, feature preservation, and computational speed.

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