In this section
@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}
}
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 -
