부산대학교 도서관

Recent compressed sensing (CS) approaches utilize the similarity and redundancy of image patches of the magnetic resonance (MR) image to enable reconstruction from highly undersampled k-space measurements. In this paper, the patches similarity and redundancy is exploited by assuming that the image patches sample a low-dimensional manifold embedded in a high dimensional space. The MR image is then reconstructed by keeping the dimension of the patch manifold as small as possible. This is achieved by using the dimension of the patch manifold of a MR image as a regularizer in the objective function. Results show that the proposed method is superior to state-of-the-art patch-based MRI reconstruction methods. The algorithm is evaluated on two datasets containing 100 MR images each. The reconstruction quality of the algorithm, gauged using the three quality metrics: peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and normalized root mean square error (NRMSE), is better than the comparison methods.