Accelerated Convergent Reconstruction for MRI with High and Low Frequency

Motivation: Theoretically, results of general unfolding network are not a convergence point of the ill-posed problem for MRI reconstruction. Goal(s): Our goal was to design an accelerated convergence unfolding network that is easier to approach the convergence point. Approach: Using accelerated gradient descent method as the framework, the proximal gradient descents of MRI high-frequency and low-frequency information are completed in a single iteration, which achieves faster convergence. Results: The reconstructed MRI of our unrolled network performs better than others. Impact: The convergence point can be effectively approximated by accelerating convergence rate, but it is still not guaranteed to be the optimal point, and further work should seek the optimal value.