Xiaobo Qu1, Di Guo2, Bende Ning1, Yingkun Hou3, Shuhui Cai1, Zhong Chen1
1Department of Electronic Science, Fujian Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, Fujian, China; 2Department of Communication Engineering, Xiamen University, Xiamen, Fujian, China; 3School of Information Science and Technology, Taishan University, Taian, Shandong, China
Undersampling the k-space data can speed up magnetic resonance imaging (MRI) at the cost of introducing the aliasing artifacts. In this paper, a patch-based directional wavelets(PBDW) is proposed to sparsify the magnetic resonance (MR) image in undersampled MRI reconstruction. First, a guide image is reconstructed from incomplete k-space data with conventional compressed sensing MRI method. Then, a parameter of PBDW, indicating the geometric direction of each image patch, is trained from the guide image and incorporated into the sparsifying transform to provide the sparse representation for the image to be reconstructed. Simulations demonstrate that trained PBDW leads to better edges than the convetional sparse MRI reconstruction methods do.