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The field of medical image reconstruction has seen roughly four types of methods.
arXiv preprint, arXiv:1903.11431
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A. R. De Pierro, “On the relation between the ISRA and the EM algorithm for positron emission tomography,” IEEE Trans. Med. Imag
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G. Gindi, M. Lee, A. Rangarajan, and I. G. Zubal, “Bayesian reconstruction of functional images using anatomical information as priors,” IEEE Trans. Med. Imag
1993
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C. Bouman and K. Sauer, “A generalized Gaussian image model for edge-preserving MAP estimation,” IEEE Trans. Im. Proc
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H. M. Hudson and R. S. Larkin, “Accelerated image reconstruction using ordered subsets of projection data,” IEEE Trans. Med. Imag
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P. Feng and Y. Bresler, “Spectrum-blind minimum-rate sampling and reconstruction of multiband signals,” in ICASSP
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Y. Bresler and P. Feng, “Spectrum-blind minimum-rate sampling and reconstruction of 2-D multiband signals,” in Proc. 3rd IEEE Int. Conf. on Image Processing, ICIP’96
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J. A. Fessler and W. L. Rogers, “Spatial resolution properties of penalized-likelihood image reconstruction methods: Space-invariant tomographs,” IEEE Trans. Im. Proc
1996
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G. Harikumar and Y. Bresler, “A new algorithm for computing sparse solutions to linear inverse problems,” in Proc. IEEE Conf. Acoust. Speech Sig. Proc
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B. A. Olshausen and D. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature
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Y. Cao and D. N. Levin, “Using prior knowledge of human anatomy to constrain MR image acquisition and reconstruction: Half k-space and full k-space techniques,” Mag. Res. Im
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C.-M. Kao, X. Pan, and C.-T. Chen, “Image restoration and reconstruction with a Bayesian approach,” Med. Phys
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H. H. Barrett, C. K. Abbey, and E. Clarkson, “Objective assessment of image quality III: ROC metrics, ideal observers and likelihood-generating functions,” J. Opt. Soc. Am. A
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M. W. Marcellin, M. J. Gormish, A. Bilgin, and M. P. Boliek, “An overview of JPEG-2000,” in Proc. Data Compression Conf
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K. P. Pruessmann, M. Weiger, P. Boernert, and P. Boesiger, “Advances in sensitivity encoding with arbitrary k-space trajectories,” Mag. Res. Med
2001
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J. C. Ye, Y. Bresler, and P. Moulin, “A self-referencing level-set method for image reconstruction from sparse fourier samples,” International Journal of Computer Vision
2002
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J. Nuyts, D. Beque, P. Dupont, and L. Mortelmans, “A concave prior penalizing relative differences for maximum-a-posteriori reconstruction in emission tomography,” IEEE Trans. Nuc. Sci
2002
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M. Vetterli, P. Marziliano, and T. Blu, “Sampling signals with finite rate of innovation,” IEEE Trans. on Signal Processing
2002
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J. A. Fessler and B. P. Sutton, “Nonuniform fast Fourier transforms using min-max interpolation,” IEEE Trans. Sig. Proc
2003
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S. De Francesco and A. M. Ferreira da Silva, “Efficient NUFFT-based direct Fourier algorithm for fan beam CT reconstruction,” in Proc. SPIE 5370 Medical Imaging: Image Proc
2004
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I. Maravic and M. Vetterli, “Sampling and reconstruction of signals with finite rate of innovation in the presence of noise,” IEEE Trans. on Signal Processing
2005
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M. N. Do and M. Vetterli, “The contourlet transform: an efficient directional multiresolution image representation,” IEEE Transactions on image processing
2005
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J.-B. Thibault, C. A. Bouman, K. D. Sauer, and J. Hsieh, “A recursive filter for noise reduction in statistical iterative tomographic imaging,” in Proc. SPIE 6065 Computational Imaging IV
2006
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K. P. Pruessmann, “Encoding and reconstruction in parallel MRI,” NMR in Biomedicine
2006
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D. L. Donoho, “Compressed sensing,” IEEE Trans. Information Theory
2006
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C. Emmanuel, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Information Theory
2006
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M. Aharon, M. Elad, and A. Bruckstein, “K-SVD : An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Processing
2006
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M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on signal processing
2006
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M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process
2006
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E. Candes, L. Demanet, D. Donoho, and L. Ying, “Fast discrete curvelet transforms,” Multiscale Modeling & Simulation
2006
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M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: The application of compressed sensing for rapid MR imaging,” Magnetic resonance in medicine
2007
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J.-B. Thibault, K. Sauer, C. Bouman, and J. Hsieh, “A three-dimensional statistical approach to improved image quality for multi-slice helical CT,” Med. Phys
2007
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Z. P. Liang, “Spatiotemporal imaging with partially separable functions,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro
2007
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K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3D transform-domain collaborative filtering,” IEEE Trans. on Image Processing
2007
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M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed sensing mri,” IEEE Signal Processing Magazine
2008
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G.-H. Chen, J. Tang, and S. Leng, “Prior image constrained compressed sensing (PICCS): A method to accurately reconstruct dynamic CT images from highly undersampled projection data sets,” Med. Phys
2008
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J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color image restoration,” IEEE Trans. on Image Processing
2008
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W. P. Segars, M. Mahesh, T. J. Beck, E. C. Frey, and B. M. W. Tsui, “Realistic CT simulation using the 4D XCAT phantom,” Med. Phys
2008
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R. Chartrand, “Fast algorithms for nonconvex compressive sensing: Mri reconstruction from very few data,” in 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
2009
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H. Pedersen, S. Kozerke, S. Ringgaard, K. Nehrke, and W. Y. Kim, “k-t pca: Temporally constrained k-t blast reconstruction using principal component analysis,” Magnetic Resonance in Medicine
2009
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A. M. Bruckstein, D. L. Donoho, and M. Elad, “From sparse solutions of systems of equations to sparse modeling of signals and images,” SIAM Review
2009
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A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM journal on imaging sciences
2009
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Z. Wang and A. C. Bovik, “Mean squared error: Love it or leave it? A new look at signal fidelity measures,” IEEE Sig. Proc. Mag
2009
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R. G. Baraniuk, E. Candes, M. Elad, and Y. Ma, “Applications of sparse representation and compressive sensing,” Proc. IEEE
2010
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X. Qu, W. Zhang, D. Guo, C. Cai, S. Cai, and Z. Chen, “Iterative thresholding compressed sensing MRI based on contourlet transform,” Inverse Problems in Science and Engineering
2010
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J. P. Haldar and Z. P. Liang, “Spatiotemporal imaging with partially separable functions: A matrix recovery approach,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro
2010
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B. Zhao, J. P. Haldar, C. Brinegar, and Z. P. Liang, “Low rank matrix recovery for real-time cardiac MRI,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro
2010
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J. Mairal, F. Bach, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res
2010
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R. Rubinstein, M. Zibulevsky, and M. Elad, “Double sparsity: Learning sparse dictionaries for sparse signal approximation,” IEEE Transactions on Signal Processing
2010
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K. Gregor and Y. LeCun, “Learning fast approximations of sparse coding,” in Proceedings of the 27th International Conference on International Conference on Machine Learning
2010
Earlier work this paper cites.
G. Wang, Y. Bresler, and V. Ntziachristos, “Guest editorial compressive sensing for biomedical imaging,” IEEE Transactions on Medical Imaging
2011
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S. Ravishankar and Y. Bresler, “MR image reconstruction from highly undersampled k-space data by dictionary learning,” IEEE transactions on medical imaging
2011
Earlier work this paper cites.
S. Gleichman and Y. C. Eldar, “Blind compressed sensing,” IEEE Transactions on Information Theory
2011
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J. Trzasko and A. Manduca, “Local versus global low-rank promotion in dynamic MRI series reconstruction,” in Proc. ISMRM
2011
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S. G. Lingala, Y. Hu, E. DiBella, and M. Jacob, “Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR,” IEEE Transactions on Medical Imaging
2011
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E. J. Candès, X. Li, Y. Ma, and J. Wright, “Robust principal component analysis?,” J. ACM
2011
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M. Akcakaya, T. A. Basha, B. Goddu, L. A. Goepfert, K. V. Kissinger, V. Tarokh, W. J. Manning, and R. Nezafat, “Low-dimensional-structure self-learning and thresholding: Regularization beyond compressed sensing for MRI Reconstruction,” Mag. Res. Med
2011
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R. Vidal, “Subspace clustering,” IEEE Signal Processing Magazine
2011
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S. Ravishankar and Y. Bresler, “Adaptive sampling design for compressed sensing MRI,” in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
2011
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Q. Xu, H. Yu, X. Mou, L. Zhang, J. Hsieh, and G. Wang, “Low-dose X-ray CT reconstruction via dictionary learning,” IEEE Trans. Med. Imag
2012
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V. Chandrasekaran, B. Recht, P. A. Parrilo, and A. S. Willsky, “The convex geometry of linear inverse problems,” Found. Comp. Math
2012
Cited alongside, same era.
B. Zhao, J. P. Haldar, A. G. Christodoulou, and Z. P. Liang, “Image reconstruction from highly undersampled ( k
2012
Cited alongside, same era.
X. Qu, D. Guo, B. Ning, Y. Hou, Y. Lin, S. Cai, and Z. Chen, “Undersampled mri reconstruction with patch-based directional wavelets,” Magnetic resonance imaging
2012
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems
2012
Cited alongside, same era.
C. H. McCollough, A. C. Bartley, R. E. Carter, B. Chen, T. A. Drees, P. Edwards, D. R. Holmes III, A. E. Huang, F. Khan, S. Leng, et al
2017
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K. Kwon, D. Kim, and H. Park, “A parallel MR imaging method using multilayer perceptron,” Medical physics
2017
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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
2017
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D. Lee, J. Yoo, and J. C. Ye, “Deep residual learning for compressed sensing MRI,” in Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on
2017
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2013
Cited alongside, same era.
S. Ravishankar and Y. Bresler, “Learning sparsifying transforms,” IEEE Transactions on Signal Processing
2013
Cited alongside, same era.
S. G. Lingala and M. Jacob, “Blind compressive sensing dynamic MRI,” IEEE Transactions on Medical Imaging
2013
Cited alongside, same era.
B. Ning, X. Qu, D. Guo, C. Hu, and Z. Chen, “Magnetic resonance image reconstruction using trained geometric directions in 2D redundant wavelets domain and non-convex optimization,” Magnetic resonance imaging
2013
Cited alongside, same era.
S. Ravishankar and Y. Bresler, “Learning overcomplete sparsifying transforms for signal processing,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
2013
Cited alongside, same era.
J. P. Haldar, “Low-rank modeling of local-space neighborhoods (LORAKS) for constrained MRI,” IEEE Transactions on Medical Imaging
2014
Cited alongside, same era.
H. Guo, C. Qiu, and N. Vaswani, “An online algorithm for separating sparse and low-dimensional signal sequences from their sum,” IEEE Transactions on Signal Processing
2014
Cited alongside, same era.
B. Trémoulhéac, N. Dikaios, D. Atkinson, and S. R. Arridge, “Dynamic mr image reconstruction - separation from undersampled ( k
2014
Cited alongside, same era.
2017
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2017
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D. Wu, K. Kim, G. El Fakhri, and Q. Li, “Iterative low-dose CT reconstruction with priors trained by artificial neural network,” IEEE transactions on medical imaging
2017
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2017
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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
2017
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R. Ge and T. Ma, “On the optimization landscape of tensor decompositions,” in Advances in Neural Information Processing Systems
2017
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FDA, “510k premarket notification of Compressed SENSE,” 2018
2018
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X. Zheng, S. Ravishankar, Y. Long, and J. A. Fessler, “PWLS-ULTRA: An efficient clustering and learning-based approach for low-dose 3D CT image reconstruction,” IEEE Trans. Med. Imag
2018
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arXiv preprint, arXiv:1809.01817
B. E. Moore, S. Ravishankar, R. R. Nadakuditi, and J. A. Fessler, “Online adaptive image reconstruction (OnAIR) using dictionary models,” IEEE Transactions on Computational Imaging · 2018
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J. Schlemper, J. Caballero, J. V. Hajnal, A. N. Price, and D. Rueckert, “A deep cascade of convolutional neural networks for dynamic MR image reconstruction,” IEEE Transactions on Medical Imaging
2018
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D. Lee, J. Yoo, S. Tak, and J. C. Ye, “Deep residual learning for accelerated MRI using magnitude and phase networks,” IEEE Transactions on Biomedical Engineering
2018
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G. Wang, J. C. Ye, K. Mueller, and J. A. Fessler, “Image reconstruction is a new frontier of machine learning,” IEEE Trans. Med. Imag
2018
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Philips white paper 4522 991 31821 Nov. 2018
L. Geerts-Ossevoort, E. . Weerdt, A. Duijndam, G. van I Jperen, H. Peeters, M. Doneva, M. Nijenhuis, and A. Huang, “Compressed SENSE,” 2018 · 2018
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G. Mazor, L. Weizman, A. Tal, and Y. C. Eldar, “Low-rank magnetic resonance fingerprinting,” Medical Physics
2018
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B. Zhao, K. Setsompop, E. Adalsteinsson, B. Gagoski, H. Ye, D. Ma, Y. Jiang, P. Ellen Grant, M. A. Griswold, and L. L. Wald, “Improved magnetic resonance fingerprinting reconstruction with low-rank and subspace modeling,” Magnetic Resonance in Medicine
2018
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A. G. Christodoulou, J. L. Shaw, C. Nguyen, Q. Yang, Y. Xie, N. Wang, and D. Li, “Magnetic resonance multitasking for motion-resolved quantitative cardiovascular imaging,” Nature Biomedical Engineering
2018
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J. Min, K. H. Jin, M. Unser, and J. C. Ye, “Grid-free localization algorithm using low-rank hankel matrix for super-resolution microscopy,” IEEE Transactions on Image Processing
2018
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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
2018
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C. Garcia-Cardona and B. Wohlberg, “Convolutional dictionary learning: A comparative review and new algorithms,” IEEE Transactions on Computational Imaging
2018
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I. Y. Chun and J. A. Fessler, “Convolutional dictionary learning: acceleration and convergence,” IEEE Trans. Im. Proc
2018
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2018
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arXiv preprint, arXiv:1802.05584
I. Y. Chun and J. A. Fessler, “Convolutional analysis operator learning: acceleration and convergence,” 2018 · 2018
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S. Ravishankar, A. Lahiri, C. Blocker, and J. A. Fessler, “Deep dictionary-transform learning for image reconstruction,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
2018
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S. Ravishankar and B. Wohlberg, “Learning multi-layer transform models,” in 2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
2018
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Y. Chun and J. A. Fessler, “Deep BCD-Net using identical encoding-decoding CNN structures for iterative image recovery,” in 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)
2018
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E. Kang, W. Chang, J. Yoo, and J. C. Ye, “Deep convolutional framelet denosing for low-dose CT via wavelet residual network,” IEEE transactions on medical imaging
2018
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J. Adler and O. Öktem, “Learned primal-dual reconstruction,” IEEE transactions on medical imaging
2018
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Y. Han and J. C. Ye, “Framing U-Net via deep convolutional framelets: Application to sparse-view CT,” IEEE Transactions on Medical Imaging
2018
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K. Hammernik, T. Klatzer, E. Kobler, M. P. Recht, D. K. Sodickson, T. Pock, and F. Knoll, “Learning a variational network for reconstruction of accelerated MRI data,” Magnetic resonance in medicine
2018
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D. Lee, J. Yoo, S. Tak, and J. C. Ye, “Deep residual learning for accelerated MRI using magnitude and phase networks,” IEEE Transactions on Biomedical Engineering
2018
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Y. Han, J. Yoo, H. H. Kim, H. J. Shin, K. Sung, and J. C. Ye, “Deep learning with domain adaptation for accelerated projection-reconstruction MR,” Magnetic resonance in medicine
2018
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B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, and M. S. Rosen, “Image reconstruction by domain-transform manifold learning,” Nature
2018
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K. Gong, J. Guan, K. Kim, X. Zhang, J. Yang, Y. Seo, G. El Fakhri, J. Qi, and Q. Li, “Iterative PET image reconstruction using convolutional neural network representation,” IEEE transactions on medical imaging
2018
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K. Gong, C. Catana, J. Qi, and Q. Li, “PET image reconstruction using deep image prior,” IEEE transactions on medical imaging
2018
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A. C. Luchies and B. C. Byram, “Deep neural networks for ultrasound beamforming,” IEEE transactions on medical imaging
2018
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E. Nehme, L. E. Weiss, T. Michaeli, and Y. Shechtman, “Deep-STORM: super-resolution single-molecule microscopy by deep learning,” Optica
2018
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2018
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Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, Y. Zhang, L. Sun, and G. Wang, “Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss,” IEEE transactions on medical imaging
2018
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T. M. Quan, T. Nguyen-Duc, and W.-K. Jeong, “Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss,” IEEE Transactions on Medical Imaging
2018
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H. Chen, Y. Zhang, Y. Chen, J. Zhang, W. Zhang, H. Sun, Y. Lv, P. Liao, J. Zhou, and G. Wang, “LEARN: Learned experts? assessment-based reconstruction network for sparse-data CT,” IEEE Transactions on Medical Imaging
2018
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D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Deep image prior,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2018
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M. U. Ghani and W. C. Karl, “Deep learning based sinogram correction for metal artifact reduction,” Electronic Imaging
2018
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H. Lee, J. Lee, H. Kim, B. Cho, and S. Cho, “Deep-neural-network-based sinogram synthesis for sparse-view ct image reconstruction,” IEEE Transactions on Radiation and Plasma Medical Sciences
2018
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M. U. Ghani and W. C. Karl, “Deep learning-based sinogram completion for low-dose ct,” in 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)
2018
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Y. Han and J. Ye, “k-space deep learning for accelerated MRI,” arXiv 2018(1805.03779)
2018
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2018
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J. C. Ye, Y. Han, and E. Cha, “Deep convolutional framelets: A general deep learning framework for inverse problems,” SIAM J. Imaging Sci
2018
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2018
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B. Gözcü, R. K. Mahabadi, Y. Li, E. Ilıcak, T. Çukur, J. Scarlett, and V. Cevher, “Learning-based compressive MRI,” IEEE Transactions on Medical Imaging
2018
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Q. Nguyen and M. Hein, “Optimization landscape and expressivity of deep CNNs,” in International Conference on Machine Learning
2018
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S. S. Du, J. D. Lee, Y. Tian, A. Singh, and B. Poczos, “Gradient descent learns one-hidden-layer cnn: Don’t be afraid of spurious local minima,” in International Conference on Machine Learning
2018
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S. Gunasekar, J. D. Lee, D. Soudry, and N. Srebro, “Implicit bias of gradient descent on linear convolutional networks,” in Advances in Neural Information Processing Systems
2018
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D. Soudry, E. Hoffer, M. S. Nacson, S. Gunasekar, and N. Srebro, “The implicit bias of gradient descent on separable data,” The Journal of Machine Learning Research
2018
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H. Shan, A. Padole, F. Homayounieh, U. Kruger, R. D. Khera, C. Nitiwarangkul, M. K. Kalra, and G. Wang, “Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction,” Nature Mach. Intel
2019
Closest in time.
Y. Hu, E. G. Levine, Q. Tian, C. J. Moran, X. Wang, V. Taviani, S. S. Vasanawala, J. A. McNab, B. A. Daniel, and B. L. Hargreaves, “Motion-robust reconstruction of multishot diffusion-weighted images without phase estimation through locally low-rank regularization,” Magnetic Resonance in Medicine
2019
Closest in time.
G. Lima da Cruz, A. Bustin, O. Jaubert, T. Schneider, R. M. Botnar, and C. Prieto, “Sparsity and locally low rank regularization for mr fingerprinting,” Magnetic Resonance in Medicine
2019
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2019
Closest in time.
Z. Li, S. Ravishankar, and Y. Long, “Image-domain multi-material decomposition using a union of cross-material models,” in Proceedings 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
2019
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L. Pfister and Y. Bresler, “Learning filter bank sparsifying transforms,” IEEE Transactions on Signal Processing
2019
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2019
Closest in time.
Y. H. Yoon, S. Khan, J. Huh, and J. C. Ye, “Efficient B-mode ultrasound image reconstruction from sub-sampled RF data using deep learning,” IEEE transactions on medical imaging
2019
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FDA, “510k premarket notification of AiCE Deep Learning Reconstruction (Canon),” 2019
2019
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FDA, “510k premarket notification of Deep Learning Image Reconstruction (GE Medical Systems),” 2019
2019
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M. U. Ghani and W. C. Karl, “Fast accurate CT metal artifact reduction using data domain deep learning,” 2019
2019
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2019
Closest in time.
M. Akçakaya, S. Moeller, S. Weingärtner, and K. Uğurbil, “Scan-specific robust artificial-neural-networks for k-space interpolation (raki) reconstruction: Database-free deep learning for fast imaging,” Magnetic Resonance in Medicine
2019
Closest in time.
E. Kang, H. J. Koo, D. H. Yang, J. B. Seo, and J. C. Ye, “Cycle-consistent adversarial denoising network for multiphase coronary CT angiography,” Medical physics
2019
Closest in time.
J. C. Ye and W. K. Sung, “Understanding geometry of encoder-decoder CNNs,” in Proceedings of the 36th International Conference on Machine Learning
2019
Closest in time.