[1] Naidich D P, Marshall C H, Gribbin C, et al. Low-dose CT of the lungs: preliminary observations[J]. Radiology, 1990, 175(3): 729-731.

    Naidich D P, Marshall C H, Gribbin C, et al. Low-dose CT of the lungs: preliminary observations[J]. Radiology, 1990, 175(3): 729-731.

[2] Mori I, Machida Y, Osanai M, et al. Photon starvation artifacts of X-ray CT: their true cause and a solution[J]. Radiological Physics & Technology, 2013, 6(1): 130-141.

    Mori I, Machida Y, Osanai M, et al. Photon starvation artifacts of X-ray CT: their true cause and a solution[J]. Radiological Physics & Technology, 2013, 6(1): 130-141.

[3] Manduca A, Yu L F, Trzasko J D, et al. Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT[J]. Medical Physics, 2009, 36(11): 4911-4919.

    Manduca A, Yu L F, Trzasko J D, et al. Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT[J]. Medical Physics, 2009, 36(11): 4911-4919.

[4] Hsieh J. Adaptive streak artifact reduction in computed tomography resulting from excessive X-ray photon noise[J]. Medical Physics, 1998, 25(11): 2139-2147.

    Hsieh J. Adaptive streak artifact reduction in computed tomography resulting from excessive X-ray photon noise[J]. Medical Physics, 1998, 25(11): 2139-2147.

[5] Kachelriess M, Watzke O, Kalender W A. Generalized multi-dimensional adaptive filtering for conventional and spiral single-slice, multi-slice, and cone-beam CT[J]. Medical Physics, 2001, 28(4): 475-490.

    Kachelriess M, Watzke O, Kalender W A. Generalized multi-dimensional adaptive filtering for conventional and spiral single-slice, multi-slice, and cone-beam CT[J]. Medical Physics, 2001, 28(4): 475-490.

[6] Wang J, Li T F, Lu H B, et al. Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomography[J]. IEEE Transactions on Medical Imaging, 2006, 25(10): 1272-1283.

    Wang J, Li T F, Lu H B, et al. Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomography[J]. IEEE Transactions on Medical Imaging, 2006, 25(10): 1272-1283.

[7] Whiting BR. Signal statistics in X-ray computed tomography[C]∥Medical Imaging 2002: Physics of Medical Imaging, International Society for Optics and Photonics, 2002, 4682: 53- 61.

    Whiting BR. Signal statistics in X-ray computed tomography[C]∥Medical Imaging 2002: Physics of Medical Imaging, International Society for Optics and Photonics, 2002, 4682: 53- 61.

[8] 许琼. X线CT不完备投影数据统计重建研究[D]. 西安: 西安交通大学, 2012.

    许琼. X线CT不完备投影数据统计重建研究[D]. 西安: 西安交通大学, 2012.

    XuQ. Statistical reconstruction methods for insufficient X-ray CT projection data[D]. Xi'an: Xi'anJiaotong University, 2012.

    XuQ. Statistical reconstruction methods for insufficient X-ray CT projection data[D]. Xi'an: Xi'anJiaotong University, 2012.

[9] Smith P R, Peters T M. Bates R H T. Image reconstruction from finite numbers of projections[J]. Journal of Physics A: Mathematical, Nuclear and General, 1973, 6(3): 361-382.

    Smith P R, Peters T M. Bates R H T. Image reconstruction from finite numbers of projections[J]. Journal of Physics A: Mathematical, Nuclear and General, 1973, 6(3): 361-382.

[10] 伍伟文, 全超, 刘丰林. 相对平行直线扫描CT滤波反投影图像重建[J]. 光学学报, 2016, 36(9): 0911009.

    伍伟文, 全超, 刘丰林. 相对平行直线扫描CT滤波反投影图像重建[J]. 光学学报, 2016, 36(9): 0911009.

    Wu W W, Quan C, Liu F L. Filtered back-projection image reconstruction algorithm for opposite parallel linear CT scanning[J]. Acta Optica Sinica, 2016, 36(9): 0911009.

    Wu W W, Quan C, Liu F L. Filtered back-projection image reconstruction algorithm for opposite parallel linear CT scanning[J]. Acta Optica Sinica, 2016, 36(9): 0911009.

[11] 李增光, 李磊, 韩玉, 等. 锥束CT圆加直线轨迹反投影滤波重建算法[J]. 光学学报, 2016, 36(9): 0911008.

    李增光, 李磊, 韩玉, 等. 锥束CT圆加直线轨迹反投影滤波重建算法[J]. 光学学报, 2016, 36(9): 0911008.

    Li Z G, Li L, Han Y, et al. A BPF-type reconstruction algorithm for circle-plus-line trajectory in cone beam CT[J]. Acta Optica Sinica, 2016, 36(9): 0911008.

    Li Z G, Li L, Han Y, et al. A BPF-type reconstruction algorithm for circle-plus-line trajectory in cone beam CT[J]. Acta Optica Sinica, 2016, 36(9): 0911008.

[12] 董继伟. CT迭代重建技术原理及其研究进展[J]. 中国医学装备, 2016, 13(10): 128-133.

    董继伟. CT迭代重建技术原理及其研究进展[J]. 中国医学装备, 2016, 13(10): 128-133.

    Dong J W. The progress on research and principles of computed tomography iterative reconstruction[J]. China Medical Equipment, 2016, 13(10): 128-133.

    Dong J W. The progress on research and principles of computed tomography iterative reconstruction[J]. China Medical Equipment, 2016, 13(10): 128-133.

[13] 张涛, 王宾, 杨立娟, 等. 第二代Curvelet变换在低剂量CT图像增强中的应用[J]. 计算机工程与应用, 2010, 46(6): 191-193.

    张涛, 王宾, 杨立娟, 等. 第二代Curvelet变换在低剂量CT图像增强中的应用[J]. 计算机工程与应用, 2010, 46(6): 191-193.

    Zhang T, Wang B, Yang L J, et al. Low dose CT image enhancement based on second generation Curvelet transform[J]. Computer Engineering and Applications, 2010, 46(6): 191-193.

    Zhang T, Wang B, Yang L J, et al. Low dose CT image enhancement based on second generation Curvelet transform[J]. Computer Engineering and Applications, 2010, 46(6): 191-193.

[14] Li Z B, Yu L F, Trzasko J D, et al. Adaptive nonlocal means filtering based on local noise level for CT denoising[J]. Medical Physics, 2014, 41(1): 011908.

    Li Z B, Yu L F, Trzasko J D, et al. Adaptive nonlocal means filtering based on local noise level for CT denoising[J]. Medical Physics, 2014, 41(1): 011908.

[15] KangD, Slomka PJ, NakazatoR, et al. Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm[C]∥ Proceedings of SPIE, 2013, 8669: 86692G.

    KangD, Slomka PJ, NakazatoR, et al. Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm[C]∥ Proceedings of SPIE, 2013, 8669: 86692G.

[16] Chen Y, Yin X, Shi L, et al. Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing[J]. Physics in Medicine & Biology, 2013, 58(16): 5803-5820.

    Chen Y, Yin X, Shi L, et al. Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing[J]. Physics in Medicine & Biology, 2013, 58(16): 5803-5820.

[17] Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.

    Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.

[18] ChenH, ZhangY, Zhang WH, et al. Low-dose CT denoising with convolutional neural network[C]∥ Proceedings of IEEE 14th International Symposium on Biomedical Imaging, 2017: 143- 146.

    ChenH, ZhangY, Zhang WH, et al. Low-dose CT denoising with convolutional neural network[C]∥ Proceedings of IEEE 14th International Symposium on Biomedical Imaging, 2017: 143- 146.

[19] Chen H, Zhang Y, Zhang W H, et al. Low-dose CT via convolutional neural network[J]. Biomedical Optics Express, 2017, 8(2): 679-694.

    Chen H, Zhang Y, Zhang W H, et al. Low-dose CT via convolutional neural network[J]. Biomedical Optics Express, 2017, 8(2): 679-694.

[20] Chen H, Zhang Y, Kalra M K, et al. Low-dose CT with a residual encoder-decoder convolutional neural network (RED-CNN)[J]. IEEE Transactions on Medical Imaging, 2017, 36(12): 2524-2535.

    Chen H, Zhang Y, Kalra M K, et al. Low-dose CT with a residual encoder-decoder convolutional neural network (RED-CNN)[J]. IEEE Transactions on Medical Imaging, 2017, 36(12): 2524-2535.

[21] IoffeS, SzegedyC. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]∥Proceedings of the 32nd International Conference on Machine Learning, 2015, 37: 448- 456.

    IoffeS, SzegedyC. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]∥Proceedings of the 32nd International Conference on Machine Learning, 2015, 37: 448- 456.

[22] He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition[C]∥Processing of IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770- 778.

    He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition[C]∥Processing of IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770- 778.

[23] Zhang K, Zuo W, Chen Y, et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 2017, 26(7): 3142-3155.

    Zhang K, Zuo W, Chen Y, et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 2017, 26(7): 3142-3155.

[24] HuangG, LiuZ, Weinberger KQ, et al. Densely connected convolutional networks[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4700- 4708.

    HuangG, LiuZ, Weinberger KQ, et al. Densely connected convolutional networks[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4700- 4708.

[25] Kingma DP, BaJ. Adam: a method for stochastic optimization[C]∥The 3rd International Conference on Learning Representations, 2015: 1- 15.

    Kingma DP, BaJ. Adam: a method for stochastic optimization[C]∥The 3rd International Conference on Learning Representations, 2015: 1- 15.