Imperial College London
Alternate

ProfessorDanielRueckert

Faculty of Engineering, Department of Computing

Professor of Visual Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Wang:2021:10.1007/978-3-030-87199-4_2,
author = {Wang, S and Qin, C and Savioli, N and Chen, C and O'Regan, D and Cook, S and Guo, Y and Rueckert, D and Bai, W},
doi = {10.1007/978-3-030-87199-4_2},
pages = {14--24},
publisher = {Springer},
title = {Joint motion correction and super resolution for cardiac segmentationvia latent optimisation},
url = {http://dx.doi.org/10.1007/978-3-030-87199-4_2},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures. However, due to the limit of acquisition duration andrespiratory/cardiac motion, stacks of multi-slice 2D images are acquired inclinical routine. The segmentation of these images provides a low-resolution representation of cardiac anatomy, which may contain artefacts caused by motion. Here we propose a novel latent optimisation framework that jointly performs motion correction and super resolution for cardiac image segmentations. Given a low-resolution segmentation as input, the framework accounts for inter-slice motion in cardiac MR imaging and super-resolves the input into a high-resolution segmentation consistent with input. A multi-view loss is incorporated to leverage information from both short-axis view and long-axis view of cardiac imaging. To solve the inverse problem, iterative optimisation is performed in a latent space, which ensures the anatomical plausibility. This alleviates the need of paired low-resolution and high-resolution images for supervised learning. Experiments on two cardiac MR datasets show that the proposed framework achieves high performance, comparable to state-of-the-art super-resolution approaches and with better cross-domain generalisability and anatomical plausibility.
AU  - Wang,S
AU  - Qin,C
AU  - Savioli,N
AU  - Chen,C
AU  - O'Regan,D
AU  - Cook,S
AU  - Guo,Y
AU  - Rueckert,D
AU  - Bai,W
DO  - 10.1007/978-3-030-87199-4_2
EP  - 24
PB  - Springer
PY  - 2021///
SP  - 14
TI  - Joint motion correction and super resolution for cardiac segmentationvia latent optimisation
UR  - http://dx.doi.org/10.1007/978-3-030-87199-4_2
UR  - http://arxiv.org/abs/2107.03887v1
UR  - https://link.springer.com/chapter/10.1007/978-3-030-87199-4_2
UR  - http://hdl.handle.net/10044/1/90279
ER  -
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