Group Sparsity Regularization Based Compressed Sensing MR Image Reconstruction

Abstract:

Exploiting the sparsity of MR signals, Compressed Sensing MR imaging (CS-MRI) is one of the most promising approaches to reconstruct a MR image with good quality from highly under-sampled k-space data. The group sparse method, which exploits additional sparse representation of the spatial group structure, can promote the overall sparsity degree, thereby leading to better reconstruction performance. In this work, an efficient superpixel/group assignment method, simple linear iterative clustering (SLIC), is incorporated to CS-MRI studies. A variable splitting strategy and classic alternating direct method is employed to solve the group sparse problem. The results indicate that the proposed method is capable of achieving significant improvements in reconstruction accuracy when compared with the state-of-the-art reconstruction methods.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 3946-3951
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.3946
• Online since: July 2014
• Authors: Xin Jin*, Ming Feng Jiang, Jie Feng
• Keywords: Group Sparse, Mr Image Reconstruction, Superpixel

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