On Retrospective k-space Subsampling schemes For Deep MRI Reconstruction

Understand

Acquiring fully-sampled MRI $k$-space data is time-consuming, and collecting accelerated data can reduce the acquisition time.

• Employing 2D Cartesian-rectilinear subsampling schemes is a conventional approach for accelerated acquisitions; however, this often results in imprecise reconstructions, even with the use of Deep Learning (DL), especially at high acceleration factors.
• Non-rectilinear or non-Cartesian trajectories can be implemented in MRI scanners as alternative subsampling options.
• This work investigates the impact of the $k$-space subsampling scheme on the quality of reconstructed accelerated MRI measurements produced by trained DL models.