  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. The
Recurrent Variational Network (RecurrentVarNet) was used as the DL-based
MRI-reconstruction architecture. Cartesian, fully-sampled multi-coil $k$-space
measurements from three datasets were retrospectively subsampled with different
accelerations using eight distinct subsampling schemes: four
Cartesian-rectilinear, two Cartesian non-rectilinear, and two non-Cartesian.
Experiments were conducted in two frameworks: scheme-specific, where a distinct
model was trained and evaluated for each dataset-subsampling scheme pair, and
multi-scheme, where for each dataset a single model was trained on data
randomly subsampled by any of the eight schemes and evaluated on data
subsampled by all schemes. In both frameworks, RecurrentVarNets trained and
evaluated on non-rectilinearly subsampled data demonstrated superior
performance, particularly for high accelerations. In the multi-scheme setting,
reconstruction performance on rectilinearly subsampled data improved when
compared to the scheme-specific experiments. Our findings demonstrate the
potential for using DL-based methods, trained on non-rectilinearly subsampled
measurements, to optimize scan time and image quality.
