k-t Group sparse: a method for accelerating dynamic MRI

Abstract

Compressed sensing (CS) is a data-reduction technique that has been applied to speed up the acquisition in MRI. However, the use of this technique in dynamic MR applications has been limited in terms of the maximum achievable reduction factor. In general, noise-like artefacts and bad temporal fidelity are visible in standard CS MRI reconstructions when high reduction factors are used. To increase the maximum achievable reduction factor, additional or prior information can be incorporated in the CS reconstruction. Here, a novel CS reconstruction method is proposed that exploits the structure within the sparse representation of a signal by enforcing the support components to be in the form of groups. These groups act like a constraint in the reconstruction. The information about the support region can be easily obtained from training data in dynamic MRI acquisitions. The proposed approach was tested in two-dimensional cardiac cine MRI with both downsampled and undersampled data. Results show that higher acceleration factors (up to 9-fold), with improved spatial and temporal quality, can be obtained with the proposed approach in comparison to the standard CS reconstructions.