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不完全角度CT图像重建的模型与算法

蔺鲁萍 王永革

蔺鲁萍, 王永革. 不完全角度CT图像重建的模型与算法[J]. 北京航空航天大学学报, 2017, 43(4): 823-830. doi: 10.13700/j.bh.1001-5965.2016.0232
引用本文: 蔺鲁萍, 王永革. 不完全角度CT图像重建的模型与算法[J]. 北京航空航天大学学报, 2017, 43(4): 823-830. doi: 10.13700/j.bh.1001-5965.2016.0232
LIN Luping, WANG Yongge. CT image reconstruction model and algorithm from few views[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(4): 823-830. doi: 10.13700/j.bh.1001-5965.2016.0232(in Chinese)
Citation: LIN Luping, WANG Yongge. CT image reconstruction model and algorithm from few views[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(4): 823-830. doi: 10.13700/j.bh.1001-5965.2016.0232(in Chinese)

不完全角度CT图像重建的模型与算法

doi: 10.13700/j.bh.1001-5965.2016.0232
基金项目: 

国家自然科学基金 91538112

详细信息
    作者简介:

    蔺鲁萍, 女, 硕士研究生。主要研究方向:稀疏表示与计算机断层成像

    王永革, 男, 博士, 硕士生导师。主要研究方向:稀疏表示与图像处理

    通讯作者:

    王永革, E-mail:wangyongge@buaa.edu.cn

  • 中图分类号: TP391;O29

CT image reconstruction model and algorithm from few views

Funds: 

National Natural Science Foundation of China 91538112

More Information
  • 摘要:

    为了提升不完全角度计算机断层成像(CT)图像的重建精度和重建效率,研究了有限角度和稀疏角度下的CT图像重建问题,提出新的全变差最小化目标函数,通过将上一步迭代重建的图像作为反馈加入到新的迭代之中,不断更新目标函数的已知项。在算法求解时,采用增广Lagrangian罚函数方法,将约束问题非约束化,并将之转化为等价的3个子问题,通过在交替方向上求解子问题来获得优化问题的最优解。实验结果表明,该算法重建出的图像信息完整,细节清晰,重建精度高,与Split Bregman算法相比,本文算法结果的相对均方误差可下降42.1%~98.5%,条纹指标可下降42.8%~98.5%。

     

  • 图 1  4幅医学的原始图像

    Figure 1.  Four original medical images

    图 2  有限角度下3种算法的重建图像

    Figure 2.  Reconstruction images of three algorithms in limited view

    图 3  有限角度下重建图像与原始图像的像素比较

    Figure 3.  Pixel comparison of reconstruction images with original images in limited view

    图 4  稀疏角度下3种算法的重建图像

    Figure 4.  Reconstruction images of three algorithms in sparse view

    图 5  稀疏角度下重建图像与原始图像的像素比较

    Figure 5.  Pixel comparison of reconstruction images with original images in sparse view

    图 6  根据RRSME和SI的值找到最优的θ

    Figure 6.  Find optimum θ according to RRSME and SI

    表  1  有限角度下不同重建算法的重建结果

    Table  1.   Reconstruction results of different reconstruction algorithms in limited view

    重建算法 Phantom 肺部 上腹部 肝部
    RRSME SI 重建时间/s RRSME SI 重建时间/s RRSME SI 重建时间/s RRSME SI 重建时间/s
    FBP 0.77 14.41 1.18 0.74 10.76 1.89 0.77 12.99 5.24 0.76 13.55 1.49
    SpBr 0.48 10.41 155.28 0.41 8.13 149.06 0.41 9.90 202.54 0.37 10.69 201.22
    UIAL 0.06 1.54 29.22 0.17 3.81 29.69 0.13 3.78 48.62 0.14 4.80 44.27
    下载: 导出CSV

    表  2  稀疏角度下不同重建算法重建结果

    Table  2.   Reconstruction results of different reconstruction algorithms in sparse view

    重建算法 Phantom 肺部 上腹部 肝部
    RRSME SI 重建时间/s RRSME SI 重建时间/s RRSME SI 重建时间/s RRSME SI 重建时间/s
    FBP 0.38 12.13 1.19 0.28 9.47 1.29 0.19 8.87 1.52 0.26 12.49 1.73
    SpBr 0.20 6.15 148.19 0.19 6.43 129.84 0.10 5.08 178.15 0.20 9.66 183.95
    UIAL 0.003 0.09 23.60 0.11 3.68 24.06 0.05 2.38 28.02 0.07 3.17 17.49
    下载: 导出CSV
  • [1] 闫镔, 李磊.CT图像重建算法[M].北京:科学出版社, 2014:97-112.

    YAN B, LI L.CT image reconstruction algorithm[M].Beijing:Science Press, 2014:97-112(in Chinese).
    [2] CANDES E, ROMBERG J, TAO T.Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information[J].IEEE Transactions on Information Theory, 2006, 52(2):489-509. doi: 10.1109/TIT.2005.862083
    [3] HASHEMI S, BEHESHTI S, GILL P R, et al.Accelerated compressed sensing based CT image reconstruction[J].Computational & Mathematical Methods in Medicine, 2015:16797. http://europepmc.org/articles/PMC4489012
    [4] TROPP J A, WRIGHT S J.Computational methods for sparse solution of linear inverse problems[J].Proceedings of the IEEE, 2010, 98(6):948-958. doi: 10.1109/JPROC.2010.2044010
    [5] WANG Y, YANG J, YIN W, et al.A new alternating minimization algorithm for total variation image reconstruction[J].SIAM Journal on Imaging Sciences, 2008, 1(3):248-272. doi: 10.1137/080724265
    [6] ZHU Z, WAHID K, BABYN P, et al.Improved compressed sensing-based algorithm for sparse-view CT image reconstruction[J].Computational & Mathematical Methods in Medicine, 2013(18):84-104. https://www.researchgate.net/profile/Paul_Babyn/publication/236255367_Improved_Compressed_Sensing-Based_Algorithm_for_Sparse-View_CT_Image_Reconstruction/links/00b7d53aadf055ea90000000.pdf?inViewer=true&pdfJsDownload=true&disableCoverPage=true&origin=publication_detail
    [7] 邓露珍, 冯鹏, 陈绵毅, 等.一种基于Contourlet变换与分裂Bregman方法的CT图像重建算法[J].CT理论与应用研究, 2014, 23(5):751-759. http://www.cnki.com.cn/Article/CJFDTOTAL-CTLL201405004.htm

    DENG L Z, FENG P, CHEN M Y, et al.An CT image reconstruction algorithm based on Contourlet transform and Split Bregman methods[J].CT Theory and Application Research, 2014, 23(5):751-759(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-CTLL201405004.htm
    [8] NIEN H, FESSLER J A.Fast X-ray CT image reconstruction using a linearized augmented Lagrangian method with ordered subsets[J].IEEE Transactions on Medical Imaging, 2014, 34(2):388-399. https://arxiv.org/abs/1402.4381
    [9] NIEN H, FESSLER J.Relaxed linearized algorithms for faster X-Ray CT image reconstruction[J].IEEE Transactions on Medical Imaging, 2016, 35(4):1090-1098. doi: 10.1109/TMI.2015.2508780
    [10] ZHANG H, HAN H, LIANG Z R, et al.Extracting information from previous full-dose CT scan for knowledge-based Bayesian reconstruction of current low-dose CT images[J].IEEE Transactions on Medical Imaging, 2016, 35(6):860-870. http://europepmc.org/abstract/MED/26561284
    [11] CHEN G J, TANG J, LENG S.Prior image constrained compressed sensing (PICCS):A method to accurately reconstruct dynamic CT images from highly undersampled projection data sets[J].Medical Physics, 2008, 35(2):660-663. doi: 10.1118/1.2836423
    [12] 袁亚湘.非线性优化计算方法[M].北京:科学出版社, 2008:158-173.

    YUAN Y X.Nonlinear optimization calculating method[M].Beijing:Science Press, 2008:158-173(in Chinese).
    [13] LI C B.An efficient algorithm for total variation regularization with applications to the single pixel camera and compressed sensing[D].Houston, TX:Rice University, 2009:18-31.
    [14] XIE S L, GUAN C T, HUANG W M, et al.Fast TV MR image reconstruction using variable splitting and accelerated alternating direction method with adaptive restart[C]//2015 IEEE International Conference.Piscataway, NJ:IEEE Press, 2015:1085-1088.
    [15] PEACEMAN D W, RACHFORD H H.The numerical solution of parabolic and elliptic differential equations[J].Journal of the Society for Industrial & Applied Mathematics, 2006, 3(1):28-41. doi: 10.1137/0103003
    [16] BARZILAI J, BORWEIN J M.Two-point step size gradient methods[J].IMA Journal of Numerical Analysis, 1988, 8(1):141-148. doi: 10.1093/imanum/8.1.141
    [17] GOLDSTEIN T, OSHER S.The split Bregman method for L1-regularized problems[J].SIAM Journal on Imaging Sciences, 2009, 2(2):323-343. doi: 10.1137/080725891
    [18] SIDDON R L.Fast calculation of the exact radiological path for a three-dimensional CT array[J].Medical Physics, 1985, 12(2):252-255. doi: 10.1118/1.595715
    [19] HESTENES M R.Multiplier and gradient methods[J].Journal of Optimization Theory & Applications, 1969, 4(5):303-320. doi: 10.1007/BF00927673
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出版历程
  • 收稿日期:  2016-03-24
  • 录用日期:  2016-09-09
  • 网络出版日期:  2017-04-20

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