| [1] Ma J, He J, Wang Y, et al. LdCT-Net: low-dose CT image reconstruction strategy driven by a deep dual network[C]// Physics of Medical Imaging. 2018:124.[2] Zhao H, Zhou YL, Zheng GL, et al. Effect of iterative reconstruction technique on image quality and radiation dose of head and neck artery CT angiography. Hainan Med J. 2017.[3] Ding Y, Hu T. Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionary learning for post-processing. Front Inform Technol Electr Eng. 2017;18(12):2001-2008.[4] Chang M, Li L, Chen Z, et al. A few-view reweighted sparsity hunting (FRESH) method for CT image reconstruction. J X-ray Sci Technol. 2013;21(2):161.[5] Chartrand R. Exact reconstruction of sparse signals via non-convex minimization. IEEE Signal Lett. 2007;14: 707-710. [6] Nikolova M, Ng MK, Zhang S, et al. Efficient reconstruction of piece-wise constant images using non-smooth non-convex minimization. SIAM J Imaging Sci. 2008;1: 2-25.[7] Sidky EY, Pan X, Reiser IS, et al. Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms. Med Phys. 2009;36(11):4920.[8] 齐宏亮. 有限投影数据CT图像迭代重建技术研究[D].广州:南方医科大学, 2017.[9] Chen M, Mi D, He P, et al. A CT reconstruction algorithm based on L1/2 regularization. Comput Math Methods Med. 2014;2014:862910.[10] Kim JH, Akram F, Choi KN. Image denoising feedback framework using split bregman approach. Expert Systems Appl. 2017.[11] Shen Y, Li Y, Mathematics SA. A new split bregman method with variate step for image deblurring and denoising with impulse noise. J Num Methods Comp Appl. 2018.[12] 王慧慧. 分布式交替方向乘子法研究[D]. 南京:南京大学, 2017.[13] Shen Z, Cheng LS. A coupled Image restoration model with non-convex non-smooth l_p wavelet frame and TV regularization. Iet Image Proc. 2016.[14] 蒋沅,苗生伟,罗华柱,等. Lp范数压缩感知图像重建优化算法[J]. 中国图象图形学报, 2017,22(4):435-442.[15] Tang Y. A new approach to solving the prior image constrained compressed sensing (PICCS) with applications in CT image reconstruction. Proc Spie. 2017;132:101322.[16] 詹雪薇. 稀疏角度CT重建的正则化方法研究[D]. 太原:中北大学, 2017.[17] 汪星星,李国成. 基于反馈神经网络的稀疏信号恢复的优化算法[J]. 计算机应用, 2017, 37(9):2590-2594.[18] Wang B, Wan W, Wang Y, et al. An Lp(0 ≤p≤1)-norm regularized image reconstruction scheme for breast DOT with non-negative-constraint. Biomed Eng Online. 2017;16(1):32.[19] 马敏,郭琪,闫超奇,等. 基于l2,p-范数的ECT图像重建算法[J].计量学报, 2017, 38(5):611-615.[20] Wang H, Lu C, Zhou J, et al. Orthogonal projection non-negative matrix factorization using alternating direction method of multipliers. J Image Graphics. 2017.[21] Barzilai J,Borwein JM.Two-point step size gradient methods. IMA J Num Anal. 1988;8(1):141-148.[22] Liu L, Lin W, Jin M. Reconstruction of sparse-view X-ray computed tomography using adaptive iterative algorithms. Pergamon Press, Inc. 2015.[23] 韩永欣,王建,刘立,等. 基于交替投影的CT图像重建算法[J]. 中国医学影像技术, 2016, 32(10):1592-1596. [24] She Y. Thresholding-based iterative selection procedures for model selection and shrinkage. ElectrJ Stat. 2009;3:384-415.[25] 吴长岳,孔慧华. 基于Lp算子的CT迭代重建算法研究[J]. 核电子学与探测技术,2014,34(4):509-512.[26] Chen H, Zhang Y, Zhang W, et al. Low-dose CT via convolutional neural network. Biomed Optics Exp. 2017;8(2):679.[27] 张丹丹. Split-Bregman算法在CT重建中的应用研究[D].太原:中北大学, 2016.[28] 牛善洲. 基于变分正则化的低剂量CT成像方法研究[D].广州:南方医科大学, 2015.[29] Yu W, Wang C, Huang M. Edge-preserving reconstruction from sparse projections of limited-angle computed tomography using ?0-regularized gradient prior. Rev Sci Instrum. 2017;88(4):043703.[30] 余维. 不完备投影数据的CT重建算法研究[D]. 重庆:重庆大学, 2014.[31] 李镜, 孙怡. 基于L1范数的微分相位衬度CT稀疏角度重建算法[J]. 光学学报, 2012, 32(3):70-76. |
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