Physics-Driven Deep Learning Methods for Fast Quantitative Magnetic Resonance Imaging: Performance improvements through integration with deep neural networks is a research paper published in IEEE Signal Processing Magazine (2023). On theSindex it has a DataRank of 0.425. It has been cited 16 times.
Scored on demand from live citation data
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
Base Score Contribution
0.425
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →National Key Research and Development Program of China
Grant: 2020YFA0712200
National Key Research and Development Program of China
Grant: 2021YFF0501503
National Natural Science Foundation of China
Grant: 12226008
National Natural Science Foundation of China
Grant: 81971611
National Natural Science Foundation of China
Grant: 62106252
National Natural Science Foundation of China
Grant: 62125111
National Natural Science Foundation of China
Grant: 12026603
National Natural Science Foundation of China
Grant: 62206273
National Natural Science Foundation of China
Grant: U21A6005
Shenzhen Science and Technology Program
Grant: RCYX20210609104444089
FWCI
2.50
Citation Percentile
0.9%
Citation Trend
Fields of Study
Keywords
Sustainable Development Goals