[1] Knoll F, Zbontar J, Sriram A, et al. fastMRI: a publicly avail-able raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning[J]. Radiology: Artificial Intelligence, 2020, 2(1): e190007.
[2] Yang G, Yu S, Dong H, et al. DAGAN: deep de-aliasing genera-tive adversarial networks for fast compressed sensing MRI reconstruction[J]. IEEE Transactions on Medical Imaging, 2017, 37(6): 1310-1321.
[3] Eo T, Jun Y, Kim T, et al. KIKI-net: cross-domain convolu-tional neural networks for reconstructing undersampled mag-netic resonance images[J]. Magnetic Resonance in Medicine, 2018, 80(5): 2188-2201.
[4] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[5] Lustig M, Donoho D L, Santos J M, et al. Compressed sensing MRI[J]. IEEE Signal Processing Magazine, 2008, 25(2): 72-82.
[6] Chen B. A research of rapid magnetic resonance imaging based on compressed sensing[D]. Chengdu: University of Electronic Science and Technology of China, 2016.陈兵. 基于压缩感知的快速核磁成像算法研究[D]. 成都: 电子科技大学, 2016.
[7] Zhang J G. Fast MRI image reconstruction based on com-pressed sensing[D]. Harbin: Harbin University of Science and Technology, 2016.张建广. 基于压缩感知的快速MRI图像重建[D]. 哈尔滨:哈尔滨科技大学, 2016.
[8] Yan S Y. Fast algorithm for parallel magnetic resonance image reconstruction based on total variations model[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2018.晏士友. 基于全变分的并行磁共振图像重建的快速算法研究[D]. 南京: 南京邮电大学, 2018.
[9] Li G Y, Hou X D, Zhou B J, et al. Reconstruction of com-pressed sensing MRI image based on discrete shearlet trans-form[J]. Application Research of Computer, 2013, 30(6):1895-1898.李国燕, 侯向丹, 周博君, 等. 基于离散剪切波的压缩感知MRI图像重建[J]. 计算机应用研究, 2013, 30(6): 1895-1898.
[10] Qu X, Guo D, Ning B, et al. Undersampled MRI recons-truction with patch-based directional wavelets[J]. Magnetic Resonance Imaging, 2012, 30(7): 964-977.
[11] Sun J, Li H, Xu Z. Deep ADMM-Net for compressive sensing MRI[C]//Proceedings of the 2016 Annual Conference on Neural Information Processing Systems, Barcelona, Dec 5-10, 2016. Red Hook: Curran Associates, 2016: 10-18.
[12] Song Y, Zhu Z, Lu Y, et al. Reconstruction of magnetic reso-nance imaging by three-dimensional dual-dictionary learning[J]. Magnetic Resonance in Medicine, 2014, 71(3): 1285-1298.
[13] Zhan Z, Cai J F, Guo D, et al. Fast multiclass dictionaries learning with geometrical directions in MRI reconstruction[J]. IEEE Transactions on Biomedical Engineering, 2015, 63(9): 1850-1861.
[14] Qin C, Schlemper J, Caballero J, et al. Convolutional recurrent neural networks for dynamic MR image reconstruction[J]. IEEE Transactions on Medical Imaging, 2018, 38(1): 280-290.
[15] Schlemper J, Caballero J, Hajnal J V, et al. A deep cascade of convolutional neural networks for MR image reconstruction[C]//LNCS 10265: Proceedings of the 25th International Con-ference on Information Processing in Medical Imaging, Boone, Jun 25-30, 2017. Berlin, Heidelberg: Springer, 2017: 647-658.
[16] Wang S, Cheng H, Ying L, et al. DeepcomplexMRI: exploiting deep residual network for fast parallel MR imaging with complex convolution[J]. Magnetic Resonance Imaging, 2020, 68: 136-147.
[17] Souza R, Lebel R M, Frayne R. A hybrid, dual domain, cascade of convolutional neural networks for magnetic reso-nance image reconstruction[C]//Proceedings of the 2019 International Conference on Medical Imaging with Deep Learning, London, Jul 8-10, 2019: 437-446.
[18] Mikolov T, Karafiát M, Burget L, et al. Recurrent neural network based language model[C]//Proceedings of the 11th Annual Conference of the International Speech Communi-cation Association, Makuhari, Sep 26-30, 2010: 1045-1048.
[19] Wang S, Yi L, Chen Q, et al. Edge-aware fully convolutional network with CRF-RNN layer for hip-pocampus segmentation[C]//Proceedings of the IEEE 8th Joint International Infor-mation Technology and Artificial Intelligence Conference, Chongqing, May 24-26, 2019. Piscataway: IEEE, 2019: 803-806.
[20] Xie K, Wen Y. LSTM-MA: a LSTM method with multi-moda-lity and adjacency constraint for brain image segmentation[C]//Proceedings of the 2019 IEEE International Conference on Image Processing, Taiwan, China, Sep 22-25, 2019. Piscat-away: IEEE, 2019: 240-244.
[21] Liu C, Xiao Z Y, Du N M. Application of improved convolu-tional neural network in medical image segmentation[J]. Journal of Frontiers of Computer Science and Technology,2019, 13(9): 1593-1603. 刘辰, 肖志勇, 杜年茂. 改进的卷积神经网络在医学图像分割上的应用[J]. 计算机科学与探索, 2019, 13(9): 1593-1603.
[22] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//Proceedings of the 2017 Annual Conference on Neural Information Processing Systems, Long Beach, Dec 4-9, 2017. Red Hook: Curran Associates, 2017: 5998-6008.
[23] Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module[C]//LNCS 11211: Proceedings of the 15th European Conference on Computer Vision, Munich, Sep 8-14, 2018. Berlin, Heidelberg: Springer, 2018: 3-19.
[24] Wang X T, Chan K C K, Yu K, et al. EDVR: video restor-ation with enhanced deformable convolutional networks[C]//Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, Jun 16-20, 2019. Piscataway: IEEE, 2019: 1954-1963.
[25] Zhu X Z, Hu H, Lin S, et al. Deformable convnets v2: more deformable, better results[C]//Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, Jun 16-20, 2019. Piscataway: IEEE, 2019: 9308-9316.
[26] Zhang Y L, Li K P, Li K, et al. Image super-resolution using very deep residual channel attention networks[C]//LNCS 11211: Proceedings of the 15th European Conference on Computer Vision, Munich, Sep 8-14, 2018. Berlin, Heidel-berg: Springer, 2018: 294-301.
[27] Zu D L, G J H. Nuclear magnetic resonance imaging: physical principles and methods[M]. Beijing: Peking University Press, 2014.俎栋林, 高家红. 核磁共振成像: 物理原理和方法[M]. 北京: 北京大学出版社, 2014.
[28] Winkelmann S, Schaeffter T, Koehler T, et al. An optimal radial profile order based on the golden ratio for time-resolved MRI[J]. IEEE Transactions on Medical Imaging, 2007, 26(1): 68-76.
[29] Kim D, Adalsteinsson E, Spielman D M, et al. Simple analytic variable density spiral design[J]. Magnetic Resonance in Medicine, 2003, 50(1): 214-219.
[30] Bridson R. Fast Poisson disk sampling in arbitrary dimensions[C]//Proceedings of the 2007 International Conference on Computer Graphics and Interactive Techniques, San Diego, Aug 5-9, 2007. New York: ACM, 2007: 22. |