Compressed sensing MRI with combined sparsifying transforms and smoothed l0 norm minimization

Xiaobo Qu, Xue Cao, Di Guo, Changwei Hu, Zhong Chen
2010 2010 IEEE International Conference on Acoustics, Speech and Signal Processing  
Undersampling the k-space is an efficient way to speed up the magnetic resonance imaging (MRI). Recently emerged compressed sensing MRI shows promising results. However, most of them only enforce the sparsity of images in single transform, e.g. total variation, wavelet, etc. In this paper, based on the principle of basis pursuit, we propose a new framework to combine sparsifying transforms in compressed sensing MRI. Each transform can efficiently represent specific feature that the other can
more » ... . This framework is implemented via the state-of-art smoothed 0 norm in overcomplete sparse decomposition. Simulation results demonstrate that the proposed method can improve image quality when comparing to single sparsifying transform.
doi:10.1109/icassp.2010.5495174 dblp:conf/icassp/QuCGHC10 fatcat:6moj55r37beshg57jvtwhafuaa