Dong Liang
Radiology, Nuclear Medicine and Imaging · with Ying · 2009–2020
A researcher in the Science corpus: 3 papers; trusts 9 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Radiology, Nuclear Medicine and Imaging — 33 citations into it; #1,463 of 3,265 by trust here, #1,136 by citations
- Computational Mechanics — 26 citations into it
- Biomedical Engineering — 12 citations into it
- Atomic and Molecular Physics, and Optics — 7 citations into it
- Signal Processing — 5 citations into it
Whom Dong Liang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Joel A. Tropp
- E.J. Candes
- Michael Lustig
- David L. Donoho
- Jarvis D. Haupt
- Shane F. Cotter
- Mark A. Griswold
- Robert D. Nowak
- Michael B. Wakin
- Justin K. Romberg
Who trusts Dong Liang overall
Papers from Dong Liang's perspective
Ordered by how strongly the researchers Dong Liang trusts endorse each paper. Same corpus, a different order for every researcher.
- Sparse MRI: The application of compressed sensing for rapid MR imaging
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Generalized autocalibrating partially parallel acquisitions (GRAPPA) (Magnetic Resonance in Medicine)
- Compressive Sensing by Random Convolution (SIAM Journal of Imaging Sciences)
- Signal Reconstruction From Noisy Random Projections (IEEE Transactions on Information Theory)
- Stable signal recovery from incomplete and inaccurate measurements
- Atomic Decomposition by Basis Pursuit
- Advances in sensitivity encoding with arbitrary k‐space trajectories (Magnetic Resonance in Medicine)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
Their papers
- Accelerating magnetic resonance imaging via deep learning (2016) — 888 citations
- Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks (2020) — 346 citations
- Accelerating SENSE using compressed sensing (2009) — 508 citations