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VS-Net: Variable Splitting Network for Accelerated Parallel MRI Reconstruction

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11767)


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

J. Duan and J. Schlemper—Contributed equally.

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  • DOI: 10.1007/978-3-030-32251-9_78
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This work was supported by the EPSRC Programme Grant (EP/P001009/1) and the British Heart Foundation (NH/17/1/32725). The TITAN Xp GPU used for this research was kindly donated by the NVIDIA Corporation.

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Correspondence to Jinming Duan .

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Duan, J. et al. (2019). VS-Net: Variable Splitting Network for Accelerated Parallel MRI Reconstruction. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham.

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