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High-resolution dynamic
31
P-MRSI using a low-rank tensor model
Chao Ma
, Bryan Clifford
, Yuchi Liu
, Yuning Gu
,
Fan Lam
, Xin Yu
,
Zhi Pei Liang
Bioengineering
Beckman Institute for Advanced Science and Technology
Electrical and Computer Engineering
Information Trust Institute
Coordinated Science Lab
Biomedical and Translational Sciences
Carl R. Woese Institute for Genomic Biology
Neuroscience Program
National Center for Supercomputing Applications (NCSA)
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peer-review
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31
P-MRSI using a low-rank tensor model'. Together they form a unique fingerprint.
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Keyphrases
High-resolution
100%
Low-rank Tensor Model
100%
31P MRSI
100%
Image Reconstruction
42%
Image Function
42%
Low-rank Tensor
42%
Imaging Data
28%
Training Data
28%
Phantom
14%
Sparse Sampling
14%
K-space
14%
Acquisition Scheme
14%
Super-resolution Reconstruction
14%
High-resolution Image
14%
Hybrid Data
14%
Space Coverage
14%
Mathematical Structure
14%
Data Correlation
14%
High Frame Rate
14%
Subspace Pursuit
14%
Tensor-based
14%
Subspace Structure
14%
Multi-dimensional Images
14%
Whole Body Scanner
14%
Engineering
High Resolution
100%
Rank Tensor
100%
Image Reconstruction
66%
Resolution Image
33%
Frame Rate
33%
Based Data Acquisition
16%
Mathematical Structure
16%
Body Scanner
16%
Computer Science
Image Reconstruction
100%
Image Function
75%
Resolution Image
50%
Training Data
50%
Mathematical Structure
25%
Earth and Planetary Sciences
Image Reconstruction
100%
Data Acquisition
75%
Medicine and Dentistry
Phosphorus 31
100%
Image Reconstruction
57%