Compressed Sensing MRI with Joint Image-Level and Patch-Level Priors

We develop a novel compressed sensing magnetic resonance imaging (CSMRI) algorithm with joint image and patch priors, where the total variation (TV) is adopted for image-level sparse prior and the expected patch log likelihood (EPLL) is used for patch-level sparse prior. The proposed joint priors capture global and local sparse nature of the MR image to promote image structures and suppress artifacts or noise, which can alleviate the limitations of previous CSMRI methods. And we derive an appropriate cost function which can be addressed by an efficient optimization scheme that iteratively alternates among l
₁
norm approximation, latent patch reconstruction and ideal image reconstruction. Final experiments are provided to show the satisfactory performance of the proposed method in MRI reconstruction, which outperforms other competitive CSMRI approaches in both subjective results and objective assessments.