Graph Regularized Sparse Coding Method for Highly Undersampled MRI Reconstruction

• Published in: 东华大学学报（英文版） Vol. 32; no. 3; pp. 434 - 441
• Main Author: 张明辉 尹子瑞 卢红阳 吴建华 刘且根
• Format: Journal Article
• Language: English
• Published: Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China 30.06.2015
• Subjects: Bregman iterative method; magnetic resonance imaging; graph regularized sparse coding; dictionary updating; alternating direction method
• ISSN: 1672-5220

The imaging speed is a bottleneck for magnetic resonance imaging（ MRI） since it appears. To alleviate this difficulty,a novel graph regularized sparse coding method for highly undersampled MRI reconstruction（ GSCMRI） was proposed. The graph regularized sparse coding showed the potential in maintaining the geometrical information of the data. In this study, it was incorporated with two-level Bregman iterative procedure that updated the data term in outer-level and learned dictionary in innerlevel. Moreover,the graph regularized sparse coding and simple dictionary updating stages derived by the inner minimization made the proposed algorithm converge in few iterations, meanwhile achieving superior reconstruction performance. Extensive experimental results have demonstrated GSCMRI can consistently recover both real-valued MR images and complex-valued MR data efficiently,and outperform the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.