Deep Generative Adversarial Networks for Compressed Sensing ( GANCS ) Automates MRI

@inproceedings{Mardani2017DeepGA,
  title={Deep Generative Adversarial Networks for Compressed Sensing ( GANCS ) Automates MRI},
  author={Morteza Mardani and Enhao Gong and Joseph Y. Cheng and Shreyas Vasanawala and Lei Xing and John M. Pauly},
  year={2017}
}
Magnetic resonance imaging (MRI) suffers from aliasing artifacts when it is highly undersampled for real-time imaging. Conventional compressed sensing (CS) MRI analytics are not however cognizant of the image diagnostic quality, and substantially trade-off accuracy for speed in real-time imaging. To cope with these challenges we put forth a novel CS framework that permeates benefits from generative adversarial networks (GAN) to modeling a manifold of MR images from historical patients… CONTINUE READING

Similar Papers

References

Publications referenced by this paper.
SHOWING 1-10 OF 20 REFERENCES

Least Squares Generative Adversarial Networks

  • 2017 IEEE International Conference on Computer Vision (ICCV)
  • 2016
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

1D partial fourier parallel MR imaging with deep convolutional neural network

Shanshan Wang, Ningbo Huang, +3 authors Dong Liang
  • In Proceedings of the 25st Annual Meeting of ISMRM,
  • 2017

Loss Functions for Image Restoration With Neural Networks

  • IEEE Transactions on Computational Imaging
  • 2017
VIEW 1 EXCERPT

Neural network MR image reconstruction with AUTOMAP: Automated transform by manifold approximation

Bo Zhu, Jeremiah Liu, Bruce Rosen, Matthew Rosen
  • In Proceedings of the 25st Annual Meeting of ISMRM,
  • 2017

Generalized magnetic resonance image reconstruction using the Berkeley Advanced Reconstruction Toolbox

Jonathan I. Tamir, Frank Ong, Joseph Y. Cheng, Martin Uecker, Michael Lustig
  • In ISMRM Workshop on Data Sampling and Image Reconstruction, Sedona,
  • 2016
VIEW 1 EXCERPT