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.
@article{arxiv.1907.10033,
title = {VS-Net: Variable splitting network for accelerated parallel MRI reconstruction},
author = {Jinming Duan and Jo Schlemper and Chen Qin and Cheng Ouyang and Wenjia Bai and Carlo Biffi and Ghalib Bello and Ben Statton and Declan P O'Regan and Daniel Rueckert},
journal= {arXiv preprint arXiv:1907.10033},
year = {2019}
}