Fetching the paper…
Reading the bibliography…
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive.
The NMR phased array
Peter B Roemer, William A Edelstein, Cecil E Hayes, Steven P Souza, and Otward M Mueller · 1990
Earlier work this paper cites.
Nonlinear total variation based noise removal algorithms
Leonid I Rudin, Stanley Osher, and Emad Fatemi · 1992
Earlier work this paper cites.
Perceptual image distortion
Patrick C Teo and David J Heeger · 1994
Earlier work this paper cites.
Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays
Daniel K Sodickson and Warren J Manning · 1997
Earlier work this paper cites.
Perceptual quality metrics applied to still image compression
Michael P Eckert and Andrew P Bradley · 1998
Earlier work this paper cites.
Current status of the digital database for screening mammography
Michael Heath, Kevin Bowyer, Daniel Kopans, P Kegelmeyer, Richard Moore, Kyong Chang, and S Munishkumaran · 1998
Earlier work this paper cites.
SENSE: sensitivity encoding for fast MRI
Klaas P Pruessmann, Markus Weiger, Markus B Scheidegger, and Peter Boesiger · 1999
Earlier work this paper cites.
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
Earlier work this paper cites.
SNR-optimality of sum-of-squares reconstruction for phased-array magnetic resonance imaging
Erik G Larsson, Deniz Erdogmus, Rui Yan, Jose C Principe, and Jeffrey R Fitzsimmons · 2003
Earlier work this paper cites.
Multiscale structural similarity for image quality assessment
Zhou Wang, Eero P Simoncelli, and Alan C Bovik · 2003
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
Earlier work this paper cites.
Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information
Emmanuel J Candès, Justin Romberg, and Terence Tao · 2006
Earlier work this paper cites.
Application of perceptual difference model on regularization techniques of parallel MR imaging
Donglai Huo, Dan Xu, Zhi-Pei Liang, and David Wilson · 2006
Earlier work this paper cites.
3d segmentation in the clinic: A grand challenge
Bram Van Ginneken, Tobias Heimann, and Martin Styner · 2007
Earlier work this paper cites.
Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging
Michael Lustig, David Donoho, and John M Pauly · 2007
Earlier work this paper cites.
Comparison and evaluation of methods for liver segmentation from CT datasets
Tobias Heimann, Bram Van Ginneken, Martin A Styner, Yulia Arzhaeva, Volker Aurich, Christian Bauer, Andreas Beck, Christoph Becker, Reinhard Beichel, György Bekes, et al · 2009
Earlier work this paper cites.
Mean squared error: Love it or leave it? a new look at signal fidelity measures
Zhou Wang and Alan C Bovik · 2009
Earlier work this paper cites.
Computer vision algorithms and applications
Richard Szeliski · 2011
Earlier work this paper cites.
FSIM: a feature similarity index for image quality assessment
Lin Zhang, Lei Zhang, Xuanqin Mou, David Zhang, et al · 2011
Earlier work this paper cites.
Lecture 6.5 - rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
Earlier work this paper cites.
Seven challenges in image quality assessment: past, present, and future research
Damon M Chandler · 2013
Cited alongside, same era.
MR image reconstruction from undersampled k-space with bayesian dictionary learning
Yue Huang, John Paisley, Xianbo Chen, Xinghao Ding, Feng Huang, and Xiao-Ping Zhang · 2013
Cited alongside, same era.
A new perceptual difference model for diagnostically relevant quantitative image quality evaluation: A preliminary study
Jun Miao, Feng Huang, Sreenath Narayan, and David L. Wilson · 2013
Cited alongside, same era.
Creation of fully sampled MR data repository for compressed sensing of the knee, 2013
Anne Marie Sawyer, Michael Lustig, Marcus Alley, Phdmartin Uecker, Patrick Virtue, Peng Lai, Shreyas Vasanawala, and Ge Healthcare · 2013
Cited alongside, same era.
Software toolbox and programming library for compressed sensing and parallel imaging
Martin Uecker, Patrick Virtue, Frank Ong, Mark J. Murphy, Marcus T. Alley, Shreyas S. Vasanawala, and Michael Lustig · 2013
Cited alongside, same era.
InverseNet: Solving inverse problems with splitting networks
Kai Fan, Qi Wei, Wenlin Wang, Amit Chakraborty, and Katherine A. Heller · 2017
Later among the works it cites.
ISMRM raw data format: a proposed standard for MRI raw datasets
Souheil J Inati, Joseph D Naegele, Nicholas R Zwart, Vinai Roopchansingh, Martin J Lizak, David C Hansen, Chia-Ying Liu, David Atkinson, Peter Kellman, Sebastian Kozerke, et al · 2017
Later among the works it cites.
Deep convolutional neural networks for accelerated dynamic magnetic resonance imaging
Christopher M. Sandino, Neerav Dixit, Joseph Y. Cheng, and Shreyas S Vasanawala · 2017
Later among the works it cites.
A deep cascade of convolutional neural networks for MR image reconstruction
Jo Schlemper, Jose Caballero, Joseph V. Hajnal, Anthony N. Price, and Daniel Rueckert · 2017
Later among the works it cites.
Accelerated magnetic resonance imaging by adversarial neural network
Ohad Shitrit and Tammy Riklin Raviv · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
ESPIRiT -an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA
Martin Uecker, Peng Lai, Mark J Murphy, Patrick Virtue, Michael Elad, John M Pauly, Shreyas S Vasanawala, and Michael Lustig · 2014
Cited alongside, same era.
Deep Learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Cited alongside, same era.
U-Net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Cited alongside, same era.
Learning a Variational Model for Compressed Sensing MRI Reconstruction
Kerstin Hammernik, Florian Knoll, Daniel K Sodickson, and Thomas Pock · 2016
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
Cited alongside, same era.
Compressed sensing dynamic MRI reconstruction using GPU-accelerated 3d convolutional sparse coding
Tran Minh Quan and Won-Ki Jeong · 2016
Cited alongside, same era.
Dmitry Ulyanov, Andrea Vedaldi, and Victor S. Lempitsky · 2017
Later among the works it cites.
ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald Summers · 2017
Later among the works it cites.
ADMM-Net: A deep learning approach for compressive sensing MRI
Yan Yang, Jian Sun, Huibin Li, and Zongben Xu · 2017
Later among the works it cites.
Loss functions for image restoration with neural networks
Hang Zhao, Orazio Gallo, Iuri Frosio, and Jan Kautz · 2017
Later among the works it cites.
Learning a variational network for reconstruction of accelerated MRI data
Kerstin Hammernik, Teresa Klatzer, Erich Kobler, Michael P. Recht, Daniel K. Sodickson, Thomas Pock, and Florian Knoll · 2018
Closest in time.
Framing U-Net via deep convolutional framelets: Application to sparse-view CT
Yoseob Han and Jong Chul Ye · 2018
Closest in time.
Deep learning for undersampled MRI reconstruction
Chang Min Hyun, Hwa Pyung Kim, Sung Min Lee, Sungchul Lee, and Jin Keun Seo · 2018
Closest in time.
Recurrent inference machines for accelerated MRI reconstruction, 2018
Kai Lonning, Patrick Putzky, Matthan W. A. Caan, and Max Welling · 2018
Closest in time.
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
Closest in time.
Simulating single-coil MRI from the responses of multiple coils
Mark Tygert and Jure Zbontar · 2018
Closest in time.
High-resolution image synthesis and semantic manipulation with conditional GANs
Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro · 2018
Closest in time.
Magnetic resonance angiography with compressed sensing: An evaluation of moyamoya disease
Takayuki Yamamoto, T Okada, Yasutaka Fushimi, Akira Yamamoto, Koji Fujimoto, Sachi Okuchi, Hikaru Fukutomi, Jun C. Takahashi, Takeshi Funaki, Susumu Miyamoto, Aurélien F. Stalder, Yutaka Natsuaki, Peter Speier, and Kaori Togashi · 2018
Closest in time.
Deeplesion: Automated mining of large-scale lesion annotations and universal lesion detection with deep learning
Ke Yan, Xiaosong Wang, Le Lu, and Ronald Summers · 2018
Closest in time.
Image reconstruction by domain-transform manifold learning
Bo Zhu, Jeremiah Z. Liu, Stephen F. Cauley, Bruce R. Rosen, and Matthew S. Rosen · 2018
Closest in time.