Learned Half-Quadratic Splitting Network for MR Image Reconstruction
This addresses faster and higher-quality MRI reconstruction for medical imaging, though it appears incremental as it builds on existing deep learning frameworks.
The paper tackles MR image reconstruction from undersampled k-space data by proposing a learned half-quadratic splitting algorithm implemented in an unrolled deep network. The method outperforms existing approaches with 1.76 dB and 2.74 dB PSNR improvements at 5× and 10× acceleration, using fewer parameters and faster 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 and LPDNet, and our method outperforms other methods in both quantitative results and qualitative results with fewer model parameters and faster reconstruction speed. Finally, we enlarge our model to achieve superior reconstruction quality, and the improvement is $1.76$ dB and $2.74$ dB over LPDNet in peak signal-to-noise ratio on $5\times$ and $10\times$ acceleration, respectively. Code for our method is publicly available at https://github.com/hellopipu/HQS-Net.