DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction
- Submitting institution
-
City, University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1273
- Type
- D - Journal article
- DOI
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10.1109/tmi.2017.2785879
- Title of journal
- IEEE Transactions on Medical Imaging
- Article number
- -
- First page
- 1310
- Volume
- 37
- Issue
- 6
- ISSN
- 0278-0062
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
10
- Research group(s)
-
-
- Citation count
- 221
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Output ranked fourth most popular article in IEEE Transactions in Medical Imaging for the year of publication (2018) and eleventh in 2020. Has led to follow-on research, including influential PNAS paper studying stability https://doi.org/10.1073/pnas.1907377117 that received coverage in medical and general press. Was also underlying research in securing two subsequent EU grants totalling €20.3 million (ERC IMI: H2020-JTI-IMI2 101005122 and ERC H2020: H2020-SC1-FA-DTS-2019-1 952172). Follow-on impact includes collaborations with the US National Institutes of Health and Cambridge University through the Gadgetron project; and commercial interest in the work from Siemens.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
