Compressed sensing reconstruction of MR phase-varied images using multi-scale complex sparsifying transform

The use of compressive sensing (CS) in applications with rapid spatial phase variations is difficult, since not only the magnitude but also phase regularization is required in the CS framework. In this article, we propose a novel image reconstruction scheme for MR phase varied images in which phase regularizer is not required in the rather simple CS reconstruction scheme. In our work, to improve the incoherence between the sampling matrix and the basis of the sparsifying transform, multi-scale eFREBAS transform domain thresholding was used. Reconstruction experiments showed that CS reconstruction using 8-scale eFREBAS transform can restore the magnitude and phase of images much better than the conventional method, especially at the region where phase changes rapidly