Leslie Ying

Radiology, Nuclear Medicine and Imaging · 2007–2020

A researcher in the Science corpus: 4 papers; trusts 10 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Radiology, Nuclear Medicine and Imaging — 35 citations into it; #1,277 of 3,265 by trust here, #1,019 by citations
• Computational Mechanics — 26 citations into it
• Biomedical Engineering — 12 citations into it
• Atomic and Molecular Physics, and Optics — 8 citations into it; #6,337 of 7,135 by trust here, #6,251 by citations
• Signal Processing — 5 citations into it

Whom Leslie Ying trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Joel A. Tropp
2. E.J. Candes
3. Justin K. Romberg
4. Michael Lustig
5. Mark A. Griswold
6. Peter Kellman

Who trusts Leslie Ying overall

1. Jeffrey A. Fessler
2. Florian Knoll
3. Tran Minh Quan
4. Ge Wang
5. Mark Murphy
6. Jong Chul Ye

Papers from Leslie Ying's perspective

Ordered by how strongly the researchers Leslie Ying trusts endorse each paper. Same corpus, a different order for every researcher.

1. Sparse MRI: The application of compressed sensing for rapid MR imaging
2. Generalized autocalibrating partially parallel acquisitions (GRAPPA) (Magnetic Resonance in Medicine)
3. Signal Reconstruction From Noisy Random Projections (IEEE Transactions on Information Theory)
4. Compressive Sensing by Random Convolution (SIAM Journal of Imaging Sciences)
5. Learning a Deep Convolutional Network for Image Super-Resolution
6. An Introduction To Compressive Sampling
7. SPIRiT: Iterative Self-consistent Parallel Imaging Reconstruction from Arbitrary k-Space (Magnetic Resonance in Medicine)
8. Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint (Magnetic Resonance in Medicine)
9. Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR (IEEE Transactions on Medical Imaging)
10. Joint image reconstruction and sensitivity estimation in SENSE (JSENSE) (Magnetic Resonance in Medicine)

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
• Joint image reconstruction and sensitivity estimation in SENSE (JSENSE) (2007) — 301 citations