A Fast Double Stochastic Proximal Method for CS-MRI Reconstruction with Multiple Wavelets

Motivation: Gu et al. [3] showed one can obtain comparable performance as the physics-guided deep learning (PG-DL) networks [4] for CS-MRI reconstruction by using multiple wavelets as the regularizers. Goal(s): Develop an efficient numerical algorithm for CS-MRI reconstruction with multiple wavelets. Approach: Study a fast double stochastic proximal method (FDSPM) for compressed sensing MRI (CS-MRI) reconstruction. Results: Our experiments demonstrate that FDSPM converges in less CPU time than classical CS algorithms for image reconstruction. Impact: Exploring efficient algorithms for multiple regularizers CS-MRI reconstruction can motivate new efficient network structures that are easy to train.