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In this work, we propose a deep learning approach for parallel magneticresonance imaging (MRI) reconstruction, termed a variable splitting network(VS-Net), for an efficient, high-quality reconstruction of undersampledmulti-coil MR data. We formulate the generalized parallel compressed sensingreconstruction as an energy minimization problem, for which a variablesplitting optimization method is derived. Based on this formulation we proposea novel, end-to-end trainable deep neural network architecture by unrolling theresulting iterative process of such variable splitting scheme. VS-Net isevaluated on complex valued multi-coil knee images for 4-fold and 6-foldacceleration factors. We show that VS-Net outperforms state-of-the-art deeplearning reconstruction algorithms, in terms of reconstruction accuracy andperceptual quality. Our code is publicly available athttps://github.com/j-duan/VS-Net.

VS-Net: Variable splitting network for accelerated parallel MRI reconstruction