Accelerating dynamic MRI by compressed sensing reconstruction from undersampled k-t space with spiral trajectories
Citations Over Time
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.
Related Papers
- → A Review of Reconstruction Algorithms in Compressive Sensing(2020)21 cited
- → Combination of compressed sensing and parallel imaging for highly-accelerated dynamic MRI(2012)22 cited
- → Fast implementation for compressive recovery of highly accelerated cardiac cine MRI using the balanced sparse model(2016)18 cited
- → Deep Learning Based Compressive Sensing for UWB Signal Reconstruction(2022)6 cited
- Compressed Sensing Based Dynamic MR Imaging: A Short Survey(2009)