An End-to-End Deep Network for Reconstructing CT Images Directly From Sparse Sinograms
暂无分享,去创建一个
Jian Lu | Baiying Lei | Chuanjiang He | Xiang-Gen Xia | Zemin Ren | Tianfu Wang | Wei Wang | X. Xia | Chuanjiang He | Tianfu Wang | Zemin Ren | Wei Wang | Jian Lu | Baiying Lei
[1] Peng Bao,et al. Convolutional Sparse Coding for Compressed Sensing CT Reconstruction , 2019, IEEE Transactions on Medical Imaging.
[2] Feng Zhao,et al. A Deep Learning Reconstruction Framework for Differential Phase-Contrast Computed Tomography With Incomplete Data , 2019, IEEE Transactions on Image Processing.
[3] Michael Unser,et al. Deep Convolutional Neural Network for Inverse Problems in Imaging , 2016, IEEE Transactions on Image Processing.
[4] Jongha Lee,et al. Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction , 2018, IEEE Transactions on Radiation and Plasma Medical Sciences.
[5] Steve B. Jiang,et al. Cine Cone Beam CT Reconstruction Using Low-Rank Matrix Factorization: Algorithm and a Proof-of-Principle Study , 2012, IEEE Transactions on Medical Imaging.
[6] E. Sidky,et al. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization , 2008, Physics in medicine and biology.
[7] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[8] Lei Zhang,et al. Low-Dose X-ray CT Reconstruction via Dictionary Learning , 2012, IEEE Transactions on Medical Imaging.
[9] Zhanli Hu,et al. Artifact removal using a hybrid-domain convolutional neural network for limited-angle computed tomography imaging , 2020, Physics in medicine and biology.
[10] Robert Hecht-Nielsen,et al. Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.
[11] Jeffrey A. Fessler,et al. Parallel MR Image Reconstruction Using Augmented Lagrangian Methods , 2011, IEEE Transactions on Medical Imaging.
[12] Hongwei Li,et al. Edge Information Diffusion-Based Reconstruction for Cone Beam Computed Laminography , 2018, IEEE Transactions on Image Processing.
[13] J. Coatrieux,et al. Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing , 2013, Physics in medicine and biology.
[14] Armando Manduca,et al. Adaptive nonlocal means filtering based on local noise level for CT denoising. , 2013, Medical physics.
[15] Fang-Fang Yin,et al. Augmentation of CBCT Reconstructed From Under-Sampled Projections Using Deep Learning , 2019, IEEE Transactions on Medical Imaging.
[16] Piotr J. Slomka,et al. Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm , 2013, Medical Imaging.
[17] C. McCollough. TU-FG-207A-04: Overview of the Low Dose CT Grand Challenge. , 2016, Medical physics.
[18] F. Natterer. The Mathematics of Computerized Tomography , 1986 .
[19] Yan Wang,et al. Statistical iterative reconstruction using adaptive fractional order regularization. , 2016, Biomedical optics express.
[20] Christine Toumoulin,et al. Dictionary learning based sinogram inpainting for CT sparse reconstruction , 2014 .
[21] Hu Chen,et al. Few‐view image reconstruction combining total variation and a high‐order norm , 2013, Int. J. Imaging Syst. Technol..
[22] Bruce R. Rosen,et al. Image reconstruction by domain-transform manifold learning , 2017, Nature.
[23] Ke Li,et al. Learning to Reconstruct Computed Tomography Images Directly From Sinogram Data Under A Variety of Data Acquisition Conditions , 2019, IEEE Transactions on Medical Imaging.
[24] D J Brenner,et al. Cancer risks from diagnostic radiology: the impact of new epidemiological data. , 2012, The British journal of radiology.
[25] L. Feldkamp,et al. Practical cone-beam algorithm , 1984 .
[26] Cong Nie,et al. Bayesian statistical reconstruction for low-dose X-ray computed tomography using an adaptive-weighting nonlocal prior , 2009, Comput. Medical Imaging Graph..
[27] A. Bruckstein,et al. K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .
[28] Hairong Zheng,et al. ADAPTIVE-NET: deep computed tomography reconstruction network with analytical domain transformation knowledge. , 2020, Quantitative imaging in medicine and surgery.
[29] Hu Chen,et al. LEARN: Learned Experts’ Assessment-Based Reconstruction Network for Sparse-Data CT , 2017, IEEE Transactions on Medical Imaging.
[30] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Yuxiang Xing,et al. A dual-domain deep learning-based reconstruction method for fully 3D sparse data helical CT , 2020, Physics in medicine and biology.
[32] Joachim Hornegger,et al. Ray Contribution Masks for Structure Adaptive Sinogram Filtering , 2012, IEEE Transactions on Medical Imaging.
[33] Yaoqin Xie,et al. A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution , 2018, IEEE Transactions on Medical Imaging.
[34] Jaejun Yoo,et al. Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network , 2018, IEEE Transactions on Medical Imaging.
[35] Gaohang Yu,et al. Sparse-view x-ray CT reconstruction via total generalized variation regularization , 2014, Physics in medicine and biology.
[36] Zhengrong Liang,et al. Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction , 2012, Physics in medicine and biology.
[37] R. Weissleder,et al. Block matching 3D random noise filtering for absorption optical projection tomography , 2010, Physics in medicine and biology.
[38] Rama Chellappa,et al. DuDoNet: Dual Domain Network for CT Metal Artifact Reduction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Jian Zhang,et al. Improved total variation based image compressive sensing recovery by nonlocal regularization , 2012, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).