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On Retrospective k-space Subsampling schemes For Deep MRI Reconstruction. (arXiv:2301.08365v5 [eess.IV] UPDATED)
cs.CV updates on arXiv.org arxiv.org
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 …
acquisition acquisitions arxiv data deep learning mri reduce retrospective space