Publications

Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  

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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