Imperial College London
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ProfessorDeclanO'Regan

Faculty of Medicine, Institute of Clinical Sciences

Professor of Imaging Sciences
 
 
 
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Contact

 

+44 (0)20 3313 1510declan.oregan

 
 
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Location

 

Imaging Sciences DepartmentHammersmith HospitalHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Meng:2022:10.1109/TMI.2022.3154599,
author = {Meng, Q and Bai, W and Liu, T and Simoes, Monteiro de Marvao A and O'Regan, D and Rueckert, D},
doi = {10.1109/TMI.2022.3154599},
journal = {IEEE Transactions on Medical Imaging},
pages = {1961--1974},
title = {MulViMotion: shape-aware 3D myocardial motion tracking from multi-view cardiac MRI},
url = {http://dx.doi.org/10.1109/TMI.2022.3154599},
volume = {41},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart. In the proposed method, a hybrid 2D/3D network is built to generate dense 3D motion fields by learning fused representations from multi-view images. To ensure that the motion estimation is consistent in 3D, a shape regularization module is introduced during training, where shape information from multi-view images is exploited to provide weak supervision to 3D motion estimation. We extensively evaluate the proposed method on 2D cine CMR images from 580 subjects of the UK Biobank study for 3D motion tracking of the left ventricular myocardium. Experimental results show that the proposed method quantitatively and qualitatively outperforms competing methods.
AU  - Meng,Q
AU  - Bai,W
AU  - Liu,T
AU  - Simoes,Monteiro de Marvao A
AU  - O'Regan,D
AU  - Rueckert,D
DO  - 10.1109/TMI.2022.3154599
EP  - 1974
PY  - 2022///
SN  - 0278-0062
SP  - 1961
TI  - MulViMotion: shape-aware 3D myocardial motion tracking from multi-view cardiac MRI
T2  - IEEE Transactions on Medical Imaging
UR  - http://dx.doi.org/10.1109/TMI.2022.3154599
UR  - https://ieeexplore.ieee.org/document/9721301
UR  - http://hdl.handle.net/10044/1/95108
VL  - 41
ER  -
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