[1]张智超,胡轶宁,秦永林,等.基于有序子窗搜索的非局部约束稀疏角度锥束CT重建算法[J].东南大学学报(自然科学版),2017,47(5):906-912.[doi:10.3969/j.issn.1001-0505.2017.05.011]
 Zhang Zhichao,Hu Yining,Qin Yonglin,et al.Cone-beam CT reconstruction algorithm from sparse view data constrained by non-local prior based on ordered sub-window search[J].Journal of Southeast University (Natural Science Edition),2017,47(5):906-912.[doi:10.3969/j.issn.1001-0505.2017.05.011]
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基于有序子窗搜索的非局部约束稀疏角度锥束CT重建算法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
47
期数:
2017年第5期
页码:
906-912
栏目:
计算机科学与工程
出版日期:
2017-09-20

文章信息/Info

Title:
Cone-beam CT reconstruction algorithm from sparse view data constrained by non-local prior based on ordered sub-window search
作者:
张智超1胡轶宁12秦永林3罗立民12
1东南大学计算机科学与工程学院, 南京 210096; 2东南大学计算机网络和信息集成教育部重点实验室, 南京 210096; 3东南大学附属中大医院血管外科, 南京 210009
Author(s):
Zhang Zhichao1 Hu Yining12 Qin Yonglin3 Luo Limin12
1 School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
2Key Laboratory of Computer Network and Information Integration of Ministry of Education, Southeast University, Nanjing 210096, China
3Department of Interventional Therapy and Vascular Surgery, Zhongda Hospital, Southeast University, Nanjing 2100096, China
关键词:
锥束CT 最大后验概率 MRF 非局部 有序子窗搜索
Keywords:
cone-beam computed tomography(CT) maximum a posteriori Markov random field non-local ordered sub-window search
分类号:
TP391
DOI:
10.3969/j.issn.1001-0505.2017.05.011
摘要:
为了在稀疏角度扫描条件下更好地去除重建图像中的条状伪影和保留细节信息,将非局部先验引入锥束CT重建.基于有序子集投影划分思想,提出了有序子窗搜索算法,用以解决锥束CT迭代重建算法中非局部先验计算量过大的问题.该算法将每一个体素的搜索窗划分为M个不重复的子窗,每次迭代中选取不同子集元素计算非局部先验约束.实验结果表明,通过非局部先验约束,可以获得质量更好的重建图像.而且无论是在主观视觉效果方面,还是在峰值信噪比和结构相似性指标等客观评价指标方面,有序子窗搜索算法和传统非局部算法的重建结果均无明显差别,但前者可以明显降低先验项的时间复杂度.
Abstract:
To remove streak artifacts and preserve detail information under the condition of sparse view scan, the non-local prior is introduced into the cone-beam computed tomography(CT)reconstruction. Based on the idea of ordered subsets in the partition of projection images, the ordered sub-window search method is proposed to solve the problem that the non-local prior calculation is too massive in the iterative reconstruction algorithm of cone-beam CT. The search window of each voxel is divided into M non-repetitive sub-windows in the proposed algorithm. Different sub-windows are selected to calculate the non-local prior in each iteration. The experimental results show that better reconstruction images can be obtained by the non-local prior. And there is no obvious difference between the proposed algorithm and the conventional non-local algorithm 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. But the time complexity of the non-local prior is reduced obviously by the former.

参考文献/References:

[1] Hu Y, Xie L, Chen Y, et al. Adaptive L0 norm constrained reconstructions for sparse-view scan in cone-beam CT[C]// Nuclear Science Symposium and Medical Imaging Conference. Seoul, Korea, 2013: 1-4.
[2] Hudson H M, Larkin R S. Accelerated image reconstruction using ordered subsets of projection data[J]. IEEE Transactions on Medical Imaging, 1994, 13(4): 601-609. DOI:10.1109/42.363108.
[3] Zhang H, Wang J, Ma J, et al. Statistical models and regularization strategies in statistical image reconstruction of low-dose X-ray CT: A survey[J]. Läkartidningen, 2014, 108(50):2660-2661.
[4] Buades A, Coll B, Morel J M. A non-local algorithm for image denoising[C]// IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA, 2005: 60-65. DOI:10.1109/cvpr.2005.38.
[5] Chen Y, Ma J, Feng Q, et al. Nonlocal prior Bayesian tomographic reconstruction[J]. Journal of Mathematical Imaging and Vision, 2008, 30(2): 133-146. DOI:10.1007/s10851-007-0042-5.
[6] Huang J, Ma J, Liu N, et al. Sparse angular CT reconstruction using non-local means based iterative-correction POCS[J]. Computers in Biology and Medicine, 2011, 41(4): 195-205. DOI:10.1016/j.compbiomed.2011.01.009.
[7] Ma J, Feng Q, Feng Y, et al. Generalized Gibbs priors based positron emission tomography reconstruction[J]. Computers in Biology and Medicine, 2010, 40(6): 565-571. DOI:10.1016/j.compbiomed.2010.03.012.
[8] Zhang Q, Liu Y, Shu H, et al. Application of regularized maximum likelihood algorithm in PET image reconstruction combined with nonlocal fuzzy anisotropic diffusion[J]. Optik-International Journal for Light and Electron Optics, 2013, 124(20): 4561-4565. DOI:10.1016/j.ijleo.2013.01.092.
[9] Kim D, Ramani S, Fessler J A. Combining ordered subsets and momentum for accelerated X-ray CT image reconstruction[J]. IEEE Transactions on Medical Imaging, 2015, 34(1): 167-178. DOI:10.1109/TMI.2014.2350962.
[10] Kim D, Pal D, Thibault J B, et al. Accelerating ordered subsets image reconstruction for X-ray CT using spatially nonuniform optimization transfer[J]. IEEE Transactions on Medical Imaging, 2013, 32(11): 1965-1978. DOI:10.1109/TMI.2013.2266898.
[11] Wang G, Jiang M. Ordered-subset simultaneous algebraic reconstruction techniques(OS-SART)[J]. Journal of X-ray Science and Technology, 2004, 12(3): 169-177.

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备注/Memo

备注/Memo:
收稿日期: 2017-02-28.
作者简介: 张智超(1991—),男,硕士生;胡轶宁(联系人),男,博士,副教授,hyn.list@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(81530060)、东南大学计算机网络和信息集成教育部重点实验室开放课题资助项目(K93-9-2016-07).
引用本文: 张智超,胡轶宁,秦永林,等.基于有序子窗搜索的非局部约束稀疏角度锥束CT重建算法[J].东南大学学报(自然科学版),2017,47(5):906-912. DOI:10.3969/j.issn.1001-0505.2017.05.011.
更新日期/Last Update: 2017-09-20