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We propose and evaluate a new MRI reconstruction method named LORAKI that trains an autocalibrated scan-specific recurrent neural network (RNN) to recover missing k-space data.
Haldar JP, Setsompop K · 1903
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Batch normalization: accelerating deep network training by reducing internal covariate shift
Ioffe S, Szegedy C · 1904
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Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R · 1958
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High-resolution inversion of finite Fourier transform data through a localised polynomial approximation
Liang ZP, Haacke EM, Thomas CW · 1989
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Constrained reconstruction methods in MR imaging
Liang ZP, Boada F, Constable T, Haacke EM, Lauterbur PC, Smith MR · 1992
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Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays
Sodickson DK, Manning WJ · 1997
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Generalized autocalibrating partially parallel acquisitions (GRAPPA)
Griswold MA, Jakob PM, Heidemann RM, Nittka M, Jellus V, Wang J, Kiefer B, Haase A · 2002
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Computational methods for inverse problems
Vogel CR · 2002
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Sparse MRI: The application of compressed sensing for rapid MR imaging
Lustig M, Donoho D, Pauly JM · 2007
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Spatiotemporal imaging with partially separable functions
Liang ZP · 2007
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Partial Fourier reconstruction through data fitting and convolution in k -space
Huang F, Lin W, Li Y · 2009
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Spatiotemporal Imaging With Partially Separable Functions: A Matrix Recovery Approach
Haldar JP, Liang ZP · 2010
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SPIRiT: Iterative self-consistent parallel imaging reconstruction from arbitrary k-space
Lustig M, Pauly JM · 2010
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Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR
Lingala SG, Hu Y, DiBella E, Jacob M · 2011
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Nonlinear GRAPPA: A kernel approach to parallel MRI reconstruction
Chang Y, Liang D, Ying L · 2011
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Parallel Reconstruction Using Null Operations
Zhang J, Liu C, Moseley ME · 2011
Cited alongside, same era.
Calibrationless parallel imaging reconstruction based on structured low-rank matrix completion
Shin PJ, Larson PEZ, Ohliger MA, Elad M, Pauly JM, Vigneron DB, Lustig M · 2014
Cited alongside, same era.
Low-rank modeling of local k-space neighborhoods (LORAKS) for constrained MRI
Haldar JP · 2014
Cited alongside, same era.
Low-Rank Modeling of Local k-Space Neighborhoods (LORAKS): Implementation and Examples for Reproducible Research
Haldar JP · 2014
Cited alongside, same era.
Autocalibrated LORAKS for fast constrained MRI reconstruction
Haldar JP · 2015
Cited alongside, same era.
Low-rank modeling of local k-space neighborhoods: from phase and support constraints to structured sparsity
A deep cascade of convolutional neural networks for dynamic MR image reconstruction
Schlemper J, Caballero J, Hajnal JV, Price AN, Rueckert D · 2018
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Image reconstruction by domain-transform manifold learning
Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS · 2018
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Deep learning with domain adaptation for accelerated projection-reconstruction MR
Han Y, Yoo J, Kim HH, Shin HJ, Sung K, Ye JC · 2018
Later among the works it cites.
Navigator-free EPI Ghost Correction with Structured Low-Rank Matrix Models: New Theory and Methods
Lobos RA, Kim TH, Hoge WS, Haldar JP · 2018
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LORAKS Software Version 2.0: Faster Implementation and Enhanced Capabilities
Kim TH, Haldar JP · 2018
Later among the works it cites.
Improving parallel imaging by jointly reconstructing multi-contrast data
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Haldar JP · 2015
Cited alongside, same era.
Accelerating magnetic resonance imaging via deep learning
Wang S, Su Z, Ying L, Peng X, Zhu S, Liang F, Feng D, Liang D · 2016
Cited alongside, same era.
P-LORAKS: Low-rank modeling of local k-space neighborhoods with parallel imaging data
Haldar JP, Zhuo J · 2016
Cited alongside, same era.
Off-the-Grid Recovery of Piecewise Constant Images from Few Fourier Samples
Ongie G, Jacob M · 2016
Cited alongside, same era.
A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix
Jin KH, Lee D, Ye JC · 2016
Cited alongside, same era.
Recent advances in parallel imaging for MRI
Hamilton J, Franson D, Seiberlich N · 2017
Cited alongside, same era.
Deep Convolutional Neural Network for Inverse Problems in Imaging
Jin KH, McCann MT, Froustey E, Unser M · 2017
Cited alongside, same era.
Bilgic B, Kim TH, Liao C, Manhard MK, Wald LL, Haldar JP, Setsompop K · 2018
Later among the works it cites.
The Fourier radial error spectrum plot: A more nuanced quantitative evaluation of image reconstruction quality
Kim TH, Haldar JP · 2018
Later among the works it cites.
Deep Generative Adversarial Neural Networks for Compressive Sensing MRI
Mardani M, Gong E, Cheng JY, Vasanawala SS, Zaharchuk G, Xing L, Pauly JM · 2019
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MoDL: Model-based deep learning architecture for inverse problems
Aggarwal HK, Mani MP, Jacob M · 2019
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Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging
Akcakaya M, Moeller S, Weingartner S, Ugurbil K · 2019
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Wave-LORAKS: Combining Wave Encoding with Structured Low-Rank Matrix Modeling for More Highly Accelerated 3D Imaging
Kim TH, Bilgic B, Polak D, Setsompop K, Haldar JP · 2019
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KerNL: Kernel-Based Nonlinear Approach to Parallel MRI Reconstruction
Lyu J, Nakarmi U, Liang D, Sheng J, Ying L · 2019
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LORAKI: Reconstruction of Undersampled k-space Data using Scan-Specific Autocalibrated Recurrent Neural Networks
Kim TH, Garg P, Haldar JP · 2019
Closest in time.
Improving the Performance of Accelerated Image Reconstruction in K-Space: The Importance of Kernel Shape
Lobos RA, Haldar JP · 2019
Closest in time.