Compressively sampled magnetic resonance imaging reconstruction based on split Bregman iteration with general...

K-space under-sampling reconstruction technology is an effective means to improve the speed of magnetic resonance imaging. Among its many reconstruction algorithms, split Bregman iteration is an effective method to solve multi-constrained models. This model often contains TV variational regularization terms, the generalized threshold shrinkage operator often used to solve TV constraints subproblem. However, when the generalized threshold shrinkage operator is performing the shrinking operation, it does not consider the inconsistency of the elements in the image matrix, which will cause the loss of image details.

In response to this problem, in this paper, a non-uniform threshold shrinkage operator was proposed to solve above TV constraints subproblem, which can dynamically adjust the shrinkage threshold by the residuals of each image element. And introduce this operator when performing Split Bregman iteration to improve the performance of generalized threshold shrinkage.

After qualitative and quantitative analysis during the experiments, it can be concluded that compared with the other three methods, the proposed method has better performance in terms of Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Transferred Edge Information(TEI) and Normalized Mutual Information(NMI), and the visual perception is better. Then we also did denoising performance analysis at different noise levels, this method also showed good robustness.

The proposed method can improve the reconstruction performance of TV constrained subproblem in split Bregman iteration, and then improve the overall performance of reconstruction algorithm. Moreover, this method also shows good denoising performance at different noise levels.