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Cardiac cine MRI is the gold standard for cardiac functional assessment, but the inherently slow acquisition process creates the necessity of reconstruction approaches for accelerated undersampled acquisitions.
SENSE: Sensitivity encoding for fast MRI
K. P. Pruessmann, M. Weiger, M. B. Scheidegger, and P. Boesiger · 1999
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The Mathematics of Computerized Tomography
F. Natterer · 2001
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Generalized autocalibrating partially parallel acquisitions (GRAPPA)
Mark A. Griswold, Peter M. Jakob, Robin M. Heidemann, Mathias Nittka, Vladimir Jellus, Jianmin Wang, Berthold Kiefer, and Axel Haase · 2002
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Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli · 2004
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Compressed Sensing MRI
Michael Lustig, David L. Donoho, Juan M. Santos, and John M. Pauly · 2008
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ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA
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Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle Gouillart, and Tony Yu · 2014
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Diederik P. Kingma and Jimmy Ba · 2014
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Hamid Mojibian and Hamidreza Pouraliakbar · 2018
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Image reconstruction by domain-transform manifold learning
Bo Zhu, Jeremiah Z. Liu, Stephen F. Cauley, Bruce R. Rosen, and Matthew S. Rosen · 2018
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A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
Jo Schlemper, Jose Caballero, Joseph V. Hajnal, Anthony N. Price, and Daniel Rueckert · 2018
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James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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Reconstruction techniques for cardiac cine MRI
Rosa-María Menchón-Lara, Federico Simmross-Wattenberg, Pablo Casaseca-de-la-Higuera, Marcos Martín-Fernández, and Carlos Alberola-López · 2019
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Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging
Mehmet Akçakaya, Steen Moeller, Sebastian Weingärtner, and Kâmil Uğurbil · 2019
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Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville · 2019
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The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies
R. Basri, D. Jacobs, Y. Kasten, and S. Kritchman · 2019
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Mathematical Models for Magnetic Resonance Imaging Reconstruction: An Overview of the Approaches, Problems, and Future Research Areas
Mariya Doneva · 2020
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Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues
Florian Knoll, Kerstin Hammernik, Chi Zhang, Steen Moeller, Thomas Pock, Daniel K. Sodickson, and Mehmet Akcakaya · 2020
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Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI With Limited Training Data
Andreas Kofler, Marc Dewey, Tobias Schaeffter, Christian Wald, and Christoph Kolbitsch · 2020
Multi-domain convolutional neural network (MD-CNN) for radial reconstruction of dynamic cardiac MRI
Hossam El-Rewaidy, Ahmed S. Fahmy, Farhad Pashakhanloo, Xiaoying Cai, Selcuk Kucukseymen, Ibolya Csecs, Ulf Neisius, Hassan Haji-Valizadeh, Bjoern Menze, and Reza Nezafat · 2021
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Neural Fields in Visual Computing and Beyond
Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar · 2022
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NeRP: Implicit Neural Representation Learning With Prior Embedding for Sparsely Sampled Image Reconstruction
Liyue Shen, John Pauly, and Lei Xing · 2022
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Replication Data for: Multi-Domain Convolutional Neural Network (MD-CNN) For Radial Reconstruction of Dynamic Cardiac MRI, 2020
Hossam El-Rewaidy · 2020
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Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity-weighted coil combination
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Golden-Angle Radial MRI: Basics, Advances, and Applications
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