A Compressed Sensing Framework for Magnetic Resonance Fingerprinting
- Submitting institution
-
University of Edinburgh
(joint submission with Heriot-Watt University)
- Unit of assessment
- 12 - Engineering
- Output identifier
- 58076815
- Type
- D - Journal article
- DOI
-
10.1137/130947246
- Title of journal
- Siam journal on imaging sciences
- Article number
- -
- First page
- 2623
- Volume
- 7
- Issue
- 4
- ISSN
- 1936-4954
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- Year of publication
- 2014
- 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
-
3
- Research group(s)
-
C - SSS
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This international collaboration presented the first mathematical foundation within a compressed sensing framework for the emerging technique of Magnetic Resonance Fingerprinting (MRF). This work led directly to £1M of EPSRC awards (EP/M019802/1, EP/M019306/1), to the EU ITN award MacSeNet (£364K) and a GE global collaboration [GE contact available] to develop this technology. The work resulted in keynote talks at iTWIST18 and WIC’19 and invited talks to Technical University of Vienna and GE global Research, US. The framework opened the way for further advances in MRF reconstruction techniques, e.g. exploiting low rank models (DOI:10.1002/mrm.26639) and fast search techniques (DOI:10.1016/j.mri.2017.07.007).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
