MOCOnet: Robust Motion Correction of Cardiovascular Magnetic Resonance T1 Mapping Using Convolutional Neural Networks

MOCOnet: Robust Motion Correction of Cardiovascular Magnetic Resonance T1 Mapping Using Convolutional Neural Networks

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 corr...

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Bibliographic Details
Main Authors: Ricardo A. Gonzales, Qiang Zhang, Bartłomiej W. Papież, Konrad Werys, Elena Lukaschuk, Iulia A. Popescu, Matthew K. Burrage, Mayooran Shanmuganathan, Vanessa M. Ferreira, Stefan K. Piechnik
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-11-01
Series:Frontiers in Cardiovascular Medicine
Subjects:
cardiovascular magnetic resonance
deep learning
image registration
ShMOLLI
T1 mapping
Online Access:https://www.frontiersin.org/articles/10.3389/fcvm.2021.768245/full

Internet

https://www.frontiersin.org/articles/10.3389/fcvm.2021.768245/full

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