Abstract #2264

Undersampled MRI Reconstruction with Trained Directions from a Guide Image

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.