Reconstruction of Dynamic MRI Tensor Data Using Frequency Low-rank and Sparsity Prior

Obtaining high spatial and temporal resolution image in dynamic magnetic resonance imaging is a huge challenge. It is effective to use low-rank and sparse prior jointly for dMRI reconstruction. However, the models represented the tensor data into a matrix, which can not completely explore the spatiotemporal correlation information. Besides, using nuclear norm as convex surrogate of the rank function to enforce the low-rank, which may lead to the sub-optimal result. Hence, we proposed the piecewise frequency tensor nuclear norm in the low-rank and joint sparse prior to reconstructed data. The proposed method was tested in cardiac cine and perfusion data.