DVS-Net: Dual-domain Variable Splitting Network for Accelerated Parallel MRI Data

Parallel imaging is an important method to accel-erate the acquisition of magnetic resonance imaging data, which can shorten the breath-hold times and reduce motion artifacts. In this paper, we propose a joint frequency domain and image domain (dual-domain) reconstruction method by introducing the full sampling condition for the undersampled multi-coil MR data. The motivation is that the dual domain method can provide more information for accurate image reconstruction. An efficient iterative algorithm is developed based on the variable splitting technique and alternating direction method of multipliers, which is unrolled into an end-to-end trainable deep neural network. We evaluate the proposed network on complex valued multi-coil knee images for both 6-fold and 8-fold acceleration factors, and compare with both variational and deep learning based reconstruction algorithms. The numerical results demonstrate that our method provides better reconstruction accuracy and perceptual quality by making using of the dual domain information. Clinical relevance: This improves the reconstruction quality for accelerated parallel MRI data both visually and quantitatively.