  The data consistency for the physical forward model is crucial in inverse
problems, especially in MR imaging reconstruction. The standard way is to
unroll an iterative algorithm into a neural network with a forward model
embedded. The forward model always changes in clinical practice, so the
learning component's entanglement with the forward model makes the
reconstruction hard to generalize. The deep learning-based proximal gradient
descent was proposed and use a network as regularization term that is
independent of the forward model, which makes it more generalizable for
different MR acquisition settings. This one-time pre-trained regularization is
applied to different MR acquisition settings and was compared to conventional
L1 regularization showing ~3 dB improvement in the peak signal-to-noise ratio.
We also demonstrated the flexibility of the proposed method in choosing
different undersampling patterns.
