[1] 庄天戈. CT原理与算法[M]. 上海: 上海交通大学出版社, 1992: 77- 97.

[2] Heang K T. An inversion formula for cone-beam reconstruction[J]. Siam Journal on Applied Mathematics, 1983, 43(3): 546-552.

[3] Bruce D S. Image reconstruction from cone-beam projections: necessary and sufficient conditions and reconstruction methods[J]. IEEE Transactions on Medical Imaging, 1985, 4(1): 14-25.

[4] Hunink M G, Gazelle G S. CT screening: a trade-off of risks, benefits, and costs[J]. Journal of Clinical Investigation, 2003, 111(11): 1612-1619.

[5] 张朝宗. 工业CT技术和原理[M]. 北京: 科学出版社, 2009: 46.

[6] 王林元, 刘宏奎, 李磊, 等. 基于稀疏优化的计算机断层成像图像不完全角度重建综述[J]. 物理学报, 2014, 63(20): 208702.

    Wang L Y, Liu H K, Li L, et al. Review of sparse optimization-based computed tomography image reconstruction from few-view projections[J]. Acta Physica Sinica, 2014, 63(20): 208702.

[7] 郭红波, 贺小伟, 侯榆青, 等. 基于非凸稀疏正则的荧光分子断层成像[J]. 光学学报, 2015, 35(7): 0717001.

    Guo H B, He X W, Hou Y Q, et al. Fluorescence molecular tomography based on nonconvex sparse regularization[J]. Acta Optica Sinica, 2015, 35(7): 0717001.

[8] Sidky E Y, Kao C M, Pan X. Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT[J]. Journal of X-Ray Science and Technology, 2006, 14(2): 119-139.

[9] Emil Y S, Pan X. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization[J]. Physics in Medicine & Biology, 2008, 53(17): 4777-4807.

[10] Liu Y, Ma J H, Fan Y, et al. Adaptive-weighted total variation minimization for sparse data toward low-dose X-ray computed tomography image reconstruction[J]. Physics in Medicine & Biology, 2012, 57(23): 7923-7956.

[11] 李毅. 有限角度三维CT图像重建算法研究[D]. 太原: 中北大学, 2011.

    LiY. The study of limited angle three-dimensional CT image reconstruction algorithm[D]. Taiyuan: North University of China, 2011.

[12] Dhawan A P, Rangayyan R M, Gordon R. Image restoration by Wiener deconvolution in limited-view computed tomography[J]. Applied Optics, 1985, 24(23): 4013-4020.

[13] Park J C, Song B, Jin S K, et al. Fast compressed sensing-based CBCT reconstruction using Barzilai-Borwein formulation for application to on-line IGRT[J]. Medical Physics, 2012, 39(3): 1207-1217.

[14] 邹晶, 孙艳勤, 张朋. 由少量投影数据快速重建图像的迭代算法[J]. 光学学报, 2009, 29(5): 1198-1204.

    Zou J, Sun Y Q, Zhang P. A fast iterative image reconstruction algorithm from few-views projection data[J]. Acta Optica Sinica, 2009, 29(5): 1198-1204.

[15] Yu H Y, Wang G. A soft-threshold filtering approach for reconstruction from a limited number of projections[J]. Physics in Medicine & Biology, 2010, 55(13): 3905-3916.

[16] Karp R M. An introduction to randomized algorithms[J]. Discrete Applied Mathematics, 1991, 34(1/2/3): 165-201.

[17] Andersson T, Lathen G, Lenz R, et al. Modified gradient search for level set based image segmentation[J]. IEEE Transactions on Image Processing, 2013, 22(2): 621-630.

[18] BianchiniS. On the Euler-Lagrange equation for a variational problem[M]. [S.l.]: Birkhäuser Basel, 2007: 61- 77.

[19] FreyJ. Introduction to stochastic search and optimization: estimation, simulation, and control[M]. Hoboken: John Wiley & Sons, 2003: 368- 369.

[20] Solis F J. Wets R J B. Minimization by random search techniques[J]. Mathematics of Operations Research, 1981, 6(1): 19-30.

[21] 陈昊, 颜秀铭, 曹福凯, 等. 粒子群优化算法在CT图像复原中的应用研究[ C]. 全国光谱仪器与分析学术研讨会, 2007.

[22] 于泽. 脑CT图像配准中自适应粒子群算法的应用研究[D]. 大连: 大连海事大学, 2010.

    YuZ. Research on cerebral CT image registration by adaptive particle swarm optimization[D]. Dalian: Dalian Maritime University, 2010.

[23] Zhao J J, Ji G H, Xia Y, et al. Cavitary nodule segmentation in computed tomography images based on self-generating neural networks and particle swarm optimization[J]. International Journal of Bio-Inspired Computation, 2015, 7(1): 567-580.

[24] IdierJ. Bayesian approach to inverse problems[M]. Hoboken: John Wiley & Sons, 2008.

[25] 卢孝强, 孙恰. 基于乘性正则化的有限角度CT重建算法[J]. 光学学报, 2010, 30(5): 1285-1290.

    Lu X Q, Sun Q. Limited angle computed tomography reconstruction algorithm based on multiplicative regularization method[J]. Acta Optica Sinica, 2010, 30(5): 1285-1290.

[26] KennedyJ. Particle swarm optimization[M] ∥ Encyclopedia of Machine Learning. New York: Springer, 2011: 760- 766.

[27] Shi X H, Liang Y C, Lee H P, et al. An improved GA and a novel PSO-GA-based hybrid algorithm[J]. Information Processing Letters, 2005, 93(5): 255-261.

[28] 高璇. 粒子群算法优化及其在图像检索中的应用研究[D]. 西安: 西安电子科技大学, 2013.

    GaoX. Research on particle swarm optimization and its application in image retrieval[D]. Xi'an:Xidian University, 2013.

[29] 方峻. 粒子群算法及其应用研究[D]. 成都: 电子科技大学, 2006.

[30] NojimaY, Chen YW, Han XH. Image and video restoration with TV/L2-norm constraint[C]. International Conference on Computer Information Systems and Industrial Applications, 2015: 642- 644.

[31] Liu X W. Efficient algorithms for hybrid regularizers based image denoising and deblurring[J]. Computers & Mathematics with Applications, 2015, 69(7): 675-687.

[32] Liu Y, Liang Z R, Ma J H, et al. Total variation-Stokes strategy for sparse-view X-ray CT image reconstruction[J]. IEEE Transactions on Medical Imaging, 2014, 33(3): 749-763.

[33] Roux N L, Schmidt M, Bach F. A stochastic gradient method with an exponential convergence rate for finite training sets[J]. Advances in Neural Information Processing Systems, 2013, 4: 2663-2671.

[34] SchmidtM, Roux NL, BachF. Minimizing finite sums with the stochastic average gradient[M]. New York: Springer-Verlag, 2017.

[35] Rini D P, Shamsuddin S M, Yuhaniz S S. Particle swarm optimization: technique, system and challenges[J]. International Journal of Computer Applications, 2011, 14(1): 19-26.