Abstract
Sparse sampling can reduce the total radiation dose received by patients in the process of CT imaging. But in this situation, the reconstruction images can be severely degraded by strip artifacts. MAP algorithm with non-local prior has been applied to two-dimensional CT reconstruction from sparse-view data and generates high-quality image. However, the applications of non-local method in three-dimensional cone-beam CT are limited by its massive calculation. In order to remove streak artifacts and preserve detail information better, with the help of CUDA, we introduce non-local model into the sparse scan imaging of CBCT. The experimental results show that better reconstruction images can be obtained by the non-local prior in terms of the subjective visual effect and the objective evaluation indices such as the peak signal-to-noise ratio and the structural similarity index.
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References
Hu Y, Xie L, Chen Y, et al. (2013) Adaptive L0 norm constrained reconstructions for sparse-view scan in cone-beam CT[C]. Nucl Sci Symp Med Imag Conf (NSS/MIC), 2013 IEEEIEEE. 1–4
Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images[J]. Pattern Anal Mach Intel, IEEE Transactions on, 1984 (6): 721-741
Buades A, Coll B (2005) Morel J M. A non-local algorithm for image denoising[C]. IEEE Comp Soc Conf Comp Vision Pattern Recog (CVPR'05) IEEE 2005(2):60–65
Zhang H, Wang J, Ma J et al (2014) Statistical models and regularization strategies in statistical image reconstruction of low-dose X-ray CT: a survey[J]. arXiv preprint arXiv 1412:1732
Zeng GL (2010) Medical image reconstruction[M]. Springer, Heidelberg
Chen Y, Ma J, Feng Q et al (2008) Nonlocal prior Bayesian tomographic reconstruction[J]. J~Math Imag Vision 30(2):133–146
Zhang Q, Liu Y, Shu H et al (2013) Application of regularized maximum likelihood algorithm in PET image reconstruction combined with nonlocal fuzzy anisotropic diffusion[J]. Optik-Int J Light Elect Opt 124(20):4561–4565
Chan C, Fulton R, Feng D D, et al. (2010) Median non-local means filtering for low SNR image denoising: application to PET with anatomical knowledge[C]. IEEE Nucl Sci Symp Med Imag Conf. IEEE, 2010: 3613–3618
Xie L, Hu Y, Yan B et al (2015) An effective CUDA parallelization of projection in iterative tomography reconstruction[J]. PLoS One 10(11):1–17
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Zhang, Z., Hu, Y., Luo, L. (2019). A Cone-Beam CT Reconstruction Algorithm Constrained by Non-local Prior from Sparse-View Data. In: Jiang, M., Ida, N., Louis, A., Quinto, E. (eds) The Proceedings of the International Conference on Sensing and Imaging. ICSI 2017. Lecture Notes in Electrical Engineering, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-319-91659-0_20
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DOI: https://doi.org/10.1007/978-3-319-91659-0_20
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