Accelerating dynamic MRI by compressed sensing reconstruction from undersampled k-t space with spiral trajectories

Abstract

Compressed sensing (CS) is a data-reduction technique that has been applied to speed up the acquisition in MRI. In this work, the feasibility of the CS framework for accelerated dynamic MRI is assessed. The fundamental condition of sparsity required in the CS framework is exploited by applying a wavelet transform and a Fourier transform along spatial and temporal directions. The second condition for CS, random sampling, is done by randomly skipping spiral interleaves in each dynamic frame. The proposed approach was tested in simulated and in vivo cardiac MRI data. Results show that higher acceleration factors, with improved spatial and temporal quality, can be obtained with the proposed approach in comparison to the standard CS reconstruction.