Journal article
MOCOnet: robust motion correction of cardiovascular magnetic resonance T1 mapping using convolutional neural networks
- Abstract:
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Background: Quantitative cardiovascular magnetic resonance (CMR) T1 mapping has shown promise for advanced tissue characterisation in routine clinical practise. However, T1 mapping is prone to motion artefacts, which affects its robustness and clinical interpretation. Current methods for motion correction on T1 mapping are model-driven with no guarantee on generalisability, limiting its widespread use. In contrast, emerging data-driven deep learning approaches have shown ... Expand abstract
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Version of record, 1.7MB)
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- Publisher copy:
- 10.3389/fcvm.2021.768245
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Authors
Bibliographic Details
- Publisher:
- Frontiers Media Publisher's website
- Journal:
- Frontiers in Cardiovascular Medicine Journal website
- Volume:
- 8
- Article number:
- 768245
- Publication date:
- 2021-11-23
- Acceptance date:
- 2021-10-27
- DOI:
- EISSN:
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2297-055X
Item Description
- Language:
- English
- Keywords:
- Pubs id:
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1215937
- Local pid:
- pubs:1215937
- Deposit date:
- 2021-12-02
Terms of use
- Copyright holder:
- Gonzales et al.
- Copyright date:
- 2021
- Rights statement:
- ©2021 Gonzales, Zhang, Papież, Werys, Lukaschuk, Popescu, Burrage, Shanmuganathan, Ferreira and Piechnik. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- Licence:
- CC Attribution (CC BY)
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