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Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process.
Deep residual learning for accelerated MRI using magnitude and phase networks,
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Compressed sensing MRI,
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Deep ADMM-Net for compressive sensing MRI,
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Loss functions for image restoration with neural networks,
H. Zhao, O. Gallo, I. Frosio, J. Kautz, · 2016
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A deep cascade of convolutional neural networks for dynamic MR image reconstruction,
J. Schlemper, J. Caballero, J. V. Hajnal, A. N. Price, D. Rueckert, · 2017
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A parallel MR imaging method using multilayer perceptron,
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Deep convolutional neural network for inverse problems in imaging,
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Learning a variational network for reconstruction of accelerated MRI data,
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Complex fully convolutional neural networks for MR image reconstruction,
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Stochastic deep compressive sensing for the reconstruction of diffusion tensor cardiac MRI,
J. Schlemper, G. Yang, P. Ferreira, A. Scott, L.-A. McGill, Z. Khalique, M. Gorodezky, M. Roehl, J. Keegan, D. Pennell, · 2018
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Translation of 1D inverse Fourier Transform of k-space to an image based on deep learning for accelerating magnetic resonance imaging,
T. Eo, H. Shin, T. Kim, Y. Jun, D. Hwang, · 2018
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KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images,
T. Eo, Y. Jun, T. Kim, J. Jang, H.-J. Lee, D. Hwang, · 2018
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Multi-channel generative adversarial network for parallel magnetic resonance image reconstruction in k-space,
P. Zhang, F. Wang, W. Xu, Y. Li, · 2018
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Adversarial and perceptual refinement for compressed sensing MRI reconstruction,
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Comparison of objective image quality metrics to expert radiologists’ scoring of diagnostic quality of MR images,
A. Mason, J. Rioux, S. E. Clarke, A. Costa, M. Schmidt, V. Keough, T. Huynh, S. Beyea, · 2019
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A hybrid, dual domain, cascade of convolutional neural networks for magnetic resonance image reconstruction,
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Recurrent inference machines for reconstructing heterogeneous MRI data,
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VS-Net: Variable splitting network for accelerated parallel MRI reconstruction,
J. Duan, J. Schlemper, C. Qin, C. Ouyang, W. Bai, C. Biffi, G. Bello, B. Statton, D. P. O’Regan, D. Rueckert, · 2019
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Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss,
T. M. Quan, T. Nguyen-Duc, W.-K. Jeong, · 2018
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DAGAN: Deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction,
G. Yang, S. Yu, H. Dong, G. Slabaugh, P. L. Dragotti, X. Ye, F. Liu, S. Arridge, J. Keegan, Y. Guo, · 2018
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Image reconstruction by domain-transform manifold learning,
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Learning-based compressive MRI,
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fastMRI: An open dataset and benchmarks for accelerated MRI,
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An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement,
R. Souza, O. Lucena, J. Garrafa, D. Gobbi, M. Saluzzi, S. Appenzeller, L. Rittner, R. Frayne, R. Lotufo, · 2018
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Visual recency bias is explained by a mixture model of internal representations,
K. Kalm, D. Norris, · 2018
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Self-supervised learning for medical image analysis using image context restoration,
L. Chen, P. Bentley, K. Mori, K. Misawa, M. Fujiwara, D. Rueckert, · 2019
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Deep iterative down-up CNN for image denoising,
S. Yu, B. Park, J. Jeong, · 2019
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A review of domain adaptation without target labels,
W. M. Kouw, M. Loog, · 2019
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Assessment of the generalization of learned image reconstruction and the potential for transfer learning,
F. Knoll, K. Hammernik, E. Kobler, T. Pock, M. P. Recht, D. K. Sodickson, · 2019
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GrappaNet: Combining parallel imaging with deep learning for multi-coil MRI reconstruction,
A. Sriram, J. Zbontar, T. Murrell, C. L. Zitnick, A. Defazio, D. K. Sodickson, · 2020
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DuDoRNet: Learning a dual-domain recurrent network for fast MRI reconstruction with deep T1 prior,
B. Zhou, S. K. Zhou, · 2020
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Dense recurrent neural networks for accelerated mri: History-cognizant unrolling of optimization algorithms,
S. A. H. Hosseini, B. Yaman, S. Moeller, M. Hong, M. Akçakaya, · 2020
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Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge,
F. Knoll, T. Murrell, A. Sriram, N. Yakubova, J. Zbontar, M. Rabbat, A. Defazio, M. J. Muckley, D. K. Sodickson, C. L. Zitnick, · 2020
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Enhanced deep-learning-based magnetic resonance image reconstruction by leveraging prior subject-specific brain imaging: Proof-of-concept using a cohort of presumed normal subjects,
R. Souza, Y. Beauferris, W. Loos, R. M. Lebel, R. Frayne, · 2020
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Calgary Normative Study: design of a prospective longitudinal study to characterise potential quantitative MR biomarkers of neurodegeneration over the adult lifespan,
C. R. McCreary, M. Salluzzi, L. B. Andersen, D. Gobbi, L. Lauzon, F. Saad, E. E. Smith, R. Frayne, · 2020
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Dual-domain cascade of U-Nets for multi-channel magnetic resonance image reconstruction,
R. Souza, M. Bento, N. Nogovitsyn, K. J. Chung, W. Loos, R. M. Lebel, R. Frayne, · 2020
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End-to-end variational networks for accelerated MRI reconstruction,
A. Sriram, J. Zbontar, T. Murrell, A. Defazio, C. L. Zitnick, N. Yakubova, F. Knoll, P. Johnson, · 2020
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Results of the 2020 fastMRI challenge for machine learning MR image reconstruction,
M. J. Muckley, B. Riemenschneider, A. Radmanesh, S. Kim, G. Jeong, J. Ko, Y. Jun, H. Shin, D. Hwang, M. Mostapha, et al., · 2021
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G. Yiasemis, N. Moriakov, D. Karkalousos, M. Caan, J. Teuwen, Direct: Deep image reconstruction toolkit, https://github.com/directgroup/direct , 2021
2021
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G. Yiasemis, C. I. Sánchez, J.-J. Sonke, J. Teuwen, · 2021
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