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

@inproceedings{Duan:2019:10.1007/978-3-030-32251-9_78,
author = {Duan, J and Schlemper, J and Qin, C and Ouyang, C and Bai, W and Biffi, C and Bello, G and Statton, B and O’Regan, DP and Rueckert, D},
doi = {10.1007/978-3-030-32251-9_78},
pages = {713--722},
publisher = {Springer International Publishing},
title = {VS-Net: variable splitting network for accelerated parallel MRI reconstruction},
url = {http://dx.doi.org/10.1007/978-3-030-32251-9_78},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data. We formulate the generalized parallel compressed sensing reconstruction as an energy minimization problem, for which a variable splitting optimization method is derived. Based on this formulation we propose a novel, end-to-end trainable deep neural network architecture by unrolling the resulting iterative process of such variable splitting scheme. VS-Net is evaluated on complex valued multi-coil knee images for 4-fold and 6-fold acceleration factors. We show that VS-Net outperforms state-of-the-art deep learning reconstruction algorithms, in terms of reconstruction accuracy and perceptual quality. Our code is publicly available at https://github.com/j-duan/VS-Net.
AU  - Duan,J
AU  - Schlemper,J
AU  - Qin,C
AU  - Ouyang,C
AU  - Bai,W
AU  - Biffi,C
AU  - Bello,G
AU  - Statton,B
AU  - O’Regan,DP
AU  - Rueckert,D
DO  - 10.1007/978-3-030-32251-9_78
EP  - 722
PB  - Springer International Publishing
PY  - 2019///
SN  - 0302-9743
SP  - 713
TI  - VS-Net: variable splitting network for accelerated parallel MRI reconstruction
UR  - http://dx.doi.org/10.1007/978-3-030-32251-9_78
UR  - https://link.springer.com/chapter/10.1007%2F978-3-030-32251-9_78
ER  -
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