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X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose.
R. J. Duffin and A. C. Schaeffer, “A class of nonharmonic Fourier series,” Transactions of the American Mathematical Society , vol. 72, no. 2, pp. 341–366, 1952
1952
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
D. L. Donoho, “Compressed sensing,” IEEE Transactions on Information Theory , vol. 52, no. 4, pp. 1289–1306, 2006
2006
Earlier work this paper cites.
E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Physics in Medicine and Biology , vol. 53, no. 17, p. 4777, 2008
2008
Earlier work this paper cites.
X. Pan, E. Y. Sidky, and M. Vannier, “Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?” Inverse Problems , vol. 25, no. 12, p. 123009, 2009
2009
Earlier work this paper cites.
J. Bian, J. H. Siewerdsen, X. Han, E. Y. Sidky, J. L. Prince, C. A. Pelizzari, and X. Pan, “Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT,” Physics in Medicine & Biology , vol. 55, no. 22, p. 6575, 2010
2010
Earlier work this paper cites.
T. P. Szczykutowicz and G.-H. Chen, “Dual energy CT using slow kVp switching acquisition and prior image constrained compressed sensing,” Physics in Medicine & Biology , vol. 55, no. 21, p. 6411, 2010
2010
Earlier work this paper cites.
Y. Lu, J. Zhao, and G. Wang, “Few-view image reconstruction with dual dictionaries,” Physics in Medicine & Biology , vol. 57, no. 1, p. 173, 2011
2011
Earlier work this paper cites.
S. Ramani and J. A. Fessler, “A splitting-based iterative algorithm for accelerated statistical X-ray CT reconstruction,” IEEE Transactions on Medical Imaging , vol. 31, no. 3, pp. 677–688, 2012
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep neural networks,” in Advances in Neural Information Processing Systems , 2012, pp. 341–349
2012
Earlier work this paper cites.
S. Abbas, T. Lee, S. Shin, R. Lee, and S. Cho, “Effects of sparse sampling schemes on image quality in low-dose CT,” Medical Physics , vol. 40, no. 11, 2013
2013
Earlier work this paper cites.
K. Kim, J. C. Ye, W. Worstell, J. Ouyang, Y. Rakvongthai, G. El Fakhri, and Q. Li, “Sparse-view spectral CT reconstruction using spectral patch-based low-rank penalty,” IEEE Transactions on Medical Imaging , vol. 34, no. 3, pp. 748–760, 2015
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Y. Chen, W. Yu, and T. Pock, “On learning optimized reaction diffusion processes for effective image restoration,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 5261–5269
2015
Earlier work this paper cites.
K. H. Jin and J. C. Ye, “Annihilating filter-based low-rank Hankel matrix approach for image inpainting,” IEEE Transactions on Image Processing , vol. 24, no. 11, pp. 3498–3511, 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
A. Vedaldi and K. Lenc, “Matconvnet: Convolutional neural networks for matlab,” in Proceedings of the 23rd ACM international conference on Multimedia . ACM, 2015, pp. 689–692
2015
Cited alongside, same era.
T. Lee, C. Lee, J. Baek, and S. Cho, “Moving beam-blocker-based low-dose cone-beam CT,” IEEE Transactions on Nuclear Science , vol. 63, no. 5, pp. 2540–2549, 2016
2016
Cited alongside, same era.
2017
Closest in time.
2017
Closest in time.
H. Chen, Y. Zhang, W. Zhang, P. Liao, K. Li, J. Zhou, and G. Wang, “Low-dose CT via convolutional neural network,” Biomedical optics express , vol. 8, no. 2, pp. 679–694, 2017
2017
Closest in time.
J. Adler and O. Öktem, “Learned primal-dual reconstruction,” arXiv preprint arXiv:1707.06474 , 2017
2017
Closest in time.
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2016
Cited alongside, same era.
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 1874–1883
2016
Cited alongside, same era.
T. Würfl, F. C. Ghesu, V. Christlein, and A. Maier, “Deep learning computed tomography,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2016, pp. 432–440
2016
Cited alongside, same era.
G. Wang, “A perspective on deep imaging,” IEEE Access , vol. 4, pp. 8914–8924, 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
K. H. Jin, D. Lee, and J. C. Ye, “A general framework for compressed sensing and parallel MRI using annihilating filter based low-rank Hankel matrix,” IEEE Trans. on Computational Imaging , vol. 2, no. 4, pp. 480–495, Dec 2016
2016
Cited alongside, same era.
G. Ongie and M. Jacob, “Off-the-grid recovery of piecewise constant images from few Fourier samples,” SIAM Journal on Imaging Sciences , vol. 9, no. 3, pp. 1004–1041, 2016
2016
Cited alongside, same era.
2017
Closest in time.
2017
Closest in time.
2017
Closest in time.
J. M. Wolterink, T. Leiner, M. A. Viergever, and I. Isgum, “Generative adversarial networks for noise reduction in low-dose CT,” IEEE Transactions on Medical Imaging , 2017
2017
Closest in time.
K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Trans. on Image Processing , vol. 26, no. 9, pp. 4509–4522, 2017
2017
Closest in time.
R. Yin, T. Gao, Y. M. Lu, and I. Daubechies, “A tale of two bases: Local-nonlocal regularization on image patches with convolution framelets,” SIAM Journal on Imaging Sciences , vol. 10, no. 2, pp. 711–750, 2017
2017
Closest in time.
J. C. Ye, J. M. Kim, K. H. Jin, and K. Lee, “Compressive sampling using annihilating filter-based low-rank interpolation,” IEEE Transactions on Information Theory , vol. 63, no. 2, pp. 777–801, Feb. 2017
2017
Closest in time.
K. H. Jin, J.-Y. Um, D. Lee, J. Lee, S.-H. Park, and J. C. Ye, “Mri artifact correction using sparse+ low-rank decomposition of annihilating filter-based Hankel matrix,” Magnetic Resonance in Medicine , vol. 78, no. 1, pp. 327–340, 2017
2017
Closest in time.
K. Kim, G. El Fakhri, and Q. Li, “Low-dose CT reconstruction using spatially encoded nonlocal penalty,” Medical Physics , vol. 44, no. 10, 2017
2017
Closest in time.
2018
Closest in time.
K. H. Jin and J. C. Ye, “Sparse and Low-Rank Decomposition of a Hankel Structured Matrix for Impulse Noise Removal,” IEEE Transactions on Image Processing , vol. 27, no. 3, pp. 1448–1461, 2018
2018
Closest in time.