Accelerated High-Dimensional MR Imaging With Sparse Sampling Using Low-Rank Tensors.

ABSTRACT: High-dimensional MR imaging often requires long data acquisition time, thereby limiting its practical applications. This paper presents a low-rank tensor based method for accelerated high-dimensional MR imaging using sparse sampling. This method represents high-dimensional images as low-rank tensors (or partially separable functions) and uses this mathematical structure for sparse sampling of the data space and for image reconstruction from highly undersampled data. More specifically, the proposed method acquires two datasets with complementary sampling patterns, one for subspace estimation and the other for image reconstruction; image reconstruction from highly undersampled data is accomplished by fitting the measured data with a sparsity constraint on the core tensor and a group sparsity

SUBMITTER: He J 

PROVIDER: S-EPMC5487008 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature