L0-compressed sensing for parallel dynamic MRI using sparse Bayesian learning

• Published in: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro pp. 1048 - 1051
• Main Authors: Hong Jung, HuiSu Yoon, Jong Chul Ye
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2011
• Subjects: Bayesian methods; Cardiac cine MRI; Coils; Compressed sensing; Image reconstruction; Magnetic resonance imaging; Minimization; motion estimation and compensation; sparse Bayesian learning
• ISBN: 1424441277; 9781424441273
• ISSN: 1945-7928; 1945-8452
• DOI: 10.1109/ISBI.2011.5872581

Since the advent of compressed sensing in dynamic MR imaging area, a number of l 1 -compressed sensing algorithms have been proposed to improve the resolution. Recently, it was shown that by solving an l p minimization problem with 0 ≤ p <; 1, the number of required measurements for an exact sparse reconstruction is more reduced than in solving an l 1 minimization. However, when 0 ≤ p <; 1, the problem is not convex and there exist many local minima. To deal with this problem, we adopted an empirical Bayesian approach called sparse Bayesian learning (SBL). The main contribution of this paper is to extend the idea for parallel dynamic MR imaging problems. By exploiting the simultaneous sparsity, the algorithm outperforms other methods, especially when the coil sensitivity map is not accurate. Numerical results confirms the theory.