  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.
