A typical clinical MR protocol includes multiple scans with different contrasts for complementary diagnostic information. Various methods have been proposed to specifically accelerate multi-contrast scans by using more complicated sparsity regularization in PI+CS.
Here we proposed a Non-iterative Deep-Learning approach to further improve existing methods for highly undersampled multi-contrast MRI reconstruction.
This method uses a Sequential+Joint+Local scheme, which takes fast PI+CS reconstruction as the initial input, uses a Deep-Network on local patches, and efficiently generates a better reconstruction for different contrasts with reduced noise and artifacts.
Experiments demonstrate the proposed method has superior performance compared with existing PI+CS methods.