Learned Half-Quadratic Splitting Network for MR Image Reconstruction

Magnetic Resonance (MR) image reconstruction from highly undersampled k-space data is critical in accelerated MR imaging (MRI) techniques. In recent years, deep learning-based methods have shown great potential in this task. This paper proposes a learned half-quadratic splitting algorithm for MR image reconstruction and implements the algorithm in an unrolled deep learning network architecture. We compare the performance of our proposed method on a public cardiac MR dataset against DC-CNN, ISTANet(+) and LPDNet, and our method outperforms other methods in both quantitative results and qualitative results. Finally, we enlarge our model to achieve superior reconstruction quality, and the improvement is 1.00 dB and 1.76 dB over LPDNet in peak signal-to-noise ratio on 5x and 10x acceleration, respectively. Code for our method is publicly available at https://github.com/hellopipu/HQS-Net.