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Ordered subsets Non-Local means constrained reconstruction for sparse view cone beam CT system

Abstract : Sparse-view sampling scans reduce the patient's radiation dose by reducing the total exposure duration. CT reconstructions under such scan mode are often accompanied by severe artifacts due to the high ill-posedness of the problem. In this paper, we use a Non-Local means kernel as a regularization constraint to reconstruct image volumes from sparse-angle sampled cone-beam CT scans. To overcome the huge computational cost of the 3D reconstruction, we propose a sequential update scheme relying on ordered subsets in the image domain. It is shown through experiments on simulated and real data and comparisons with other methods that the proposed approach is robust enough to deal with the number of views reduced up to 1/10. When coupled with a CUDA parallel computing technique, the computation speed of the iterative reconstruction is greatly improved.
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-02364097
Contributor : Laurent Jonchère <>
Submitted on : Thursday, November 14, 2019 - 4:53:50 PM
Last modification on : Wednesday, January 29, 2020 - 12:01:39 PM

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Yining Hu, Zheng Wang, Lizhe Xie, Limin Luo. Ordered subsets Non-Local means constrained reconstruction for sparse view cone beam CT system. Australasian Physical and Engineering Sciences in Medicine, Springer Verlag, 2019, 42 (4), pp.1117-1128. ⟨10.1007/s13246-019-00811-z⟩. ⟨hal-02364097⟩

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