A deep cascade of convolutional neural networks for dynamic MR image reconstruction
- Submitting institution
-
Imperial College of Science, Technology and Medicine
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2148
- Type
- D - Journal article
- DOI
-
10.1109/TMI.2017.2760978
- Title of journal
- IEEE Transactions on Medical Imaging
- Article number
- -
- First page
- 491
- Volume
- 37
- Issue
- 2
- ISSN
- 0278-0062
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
10.1109/TMI.2017.2760978
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 241
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- First ever deep learning reconstruction of cardiac Magnetic Resonance Images (MRI). Key output that resulted in £5.1M SmartHeart EPSRC programme grant (http://wp.doc.ic.ac.uk/smartheart). Extended version of well-cited IPMI 2017 paper (https://link.springer.com/chapter/10.1007/978-3-319-59050-9_51). The paper led to several invitations for conference keynotes, including IS3R 2018, CMR 2018 and ISICDM 2018. It also resulted in Schlemper's offer of an internship and now a senior researcher position at Hyperfine (www.hyperfine.io, $400M start-up building low-field MRI scanners) to further develop deep learning-based MRI reconstruction techniques.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
