Accelerating magnetic resonance imaging via deep learning

By Shanshan Wang, Zhenghang Su, Leslie Ying, Xi Peng, Shun Zhu, Feng Liang, D. Feng, Dong Liang. 2016

888 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Scan-specific Robust Artificial-neural-networks for k-space Interpolation (RAKI) Reconstruction: Database-free Deep Learning for Fast Imaging (2018, methodology): “In RAKI, these functions f j,m are approximated using CNNs, and their parameters are learned from the ACS data.”
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction (2017, background): “Despite the popularity of CNNs, there has only been preliminary research on CNN-based MR image reconstruction [18], [19], hence the applicability of CNNs to this problem for various imaging protocols has yet to be fully explored.”
Convolutional Neural Networks for Inverse Problems in Imaging: A Review (2017, methodology): “The biomedical imaging papers tended to have the fewest samples (e.g., 500 brain images [21] or 2000 CT images [24]), while papers on natural images had the most (e.g., pretraining on 395,909 natural images [20]).”
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction (2018, methodology): “4) DeepLearning-BasedCS-MRI: Deep learning based studies [42], [43] propose to incorporate a CNN into CS-MRI reconstruction, that is in which f cnn is the forward propagation of data through the CNN parametrised by θ , and ζ is another regularisation parameter.”
Assessment of the generalization of learned image reconstruction and the potential for transfer learning (2019, positive): “Wang et al proposed to augment a conventional compressed-sensing reconstruction with a regularizer that is based on a convolutional neural network.(2) Kwon et al proposed to learn a parallel imaging reconstruction without explicit use of coil sensitivity maps that operates entirely within image spac”
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network (2017, neutral): “In MR image reconstruction, Wang et al [10] was the ﬁrst to apply deep learning to compressed sensing MRI (CS-MRI).”
Noise2Noise: Learning Image Restoration without Clean Data (2018, result): “Here the training with clean targets is similar to prior art (Wang et al., 2016; Lee et al., 2017).”
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI (2018, methodology): “ction process. The last two years have seen the rapid development of machine learning approaches for MR image reconstruction, which hold great promise for further acceleration of MR image acquisition [10,48,11,35,60]. Some of the rst work on this subject was presented at the 2016 annual meeting of t”
KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images (2018, methodology): “The second limitation of the earlier studies is that the network depth was shallower (3 to 5 layers) than that of most networks used in recent imagerestoration studies.(19-22) In some studies, deep CNNs with layer depths greater than 20 afford much more promising results than shallower networks beca”
Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks (2020, neutral): “In this section, we start with deep-learning methods that adopt standard networks only for fast MRI (some are summarized in Table 1) and then introduce methods that also incorporate additional MRI domain knowledge into the standard networks [13]–[28].”
Deep Learning in Neuroradiology (2018, background): “10 11-13 Also, one could apply deep learning methods to improve image quality.”
Deep Learning with Domain Adaptation for Accelerated Projection Reconstruction MR (2017, methodology): “Recently, Wang et al [13] applied deep learning to CS-MRI.”
Deep learning for undersampled MRI reconstruction (2017, result): “It should be mentioned that, at the time of writing this paper, we found that there is another work that proposes almost the same approach as [5], [9], [18].”
Learning a Variational Network for Reconstruction of Accelerated MRI Data (2017, background): “(40) showed first results using a convolutional neural network (CNN) architecture to define a relationship between zero filled solution and high-quality images based on pseudo-random sampling.”
Phase recovery and holographic image reconstruction using deep learning in neural networks (2017, positive): “Deep learning has recently been applied to solving inverse problems in imaging science such as in super-resolution 43,44 , acceleration of the image acquisition speed of computed tomography (CT) 45 , magnetic resonance imaging (MRI) 46 , photoacoustic tomography 47 and holography 48,49 .”