Zhong Chen1, Changwei Hu1, Xiaobo Qu1, Lijun Bao1, Shuhui Cai1
1Department of Electronic Science, Fujian Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, Fujian, China
Undersampling k-space is an effective way to reduce acquisition time in MRI. However, this will introduce significant aliasing artifacts, and blur edges in the reconstructed magnetic resonance image. The edges usually contain important information for the clinical diagnosis. In this work, we propose a method to recover edges from undersampled MRI by incoporating a weighting matrix to the l1 norm sparsity regularization term. Compared with conventional compressive sensing methods, the proposed method yields better edge recovery, and requires fewer k-space measurements to achieve acceptable reconstruction quality.