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MOCOnet: robust motion correction of cardiovascular magnetic resonance T1 mapping using convolutional neural networks

Abstract:

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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Publisher copy:
10.3389/fcvm.2021.768245

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Institution:
University of Oxford
Division:
MSD
Department:
RDM
Sub department:
RDM Cardiovascular Medicine
Oxford college:
Balliol College
Role:
Author
ORCID:
0000-0002-9384-4602
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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:
2297-055X
Language:
English
Keywords:
Pubs id:
1215937
Local pid:
pubs:1215937
Deposit date:
2021-12-02

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