Improving Edge Recovery in Udersampled MRI Reconstruction

Zhong Chen¹, Changwei Hu¹, Xiaobo Qu¹, Lijun Bao¹, Shuhui Cai¹

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.