In this work, we address the question if variable density sampling of 2D Cartesian knee sequences can improve deep learning-based MRI reconstruction. Our results suggest that incoherent artifacts introduced by variable density sampling are beneficial to reconstruct highly accelerated sequences. Additionally, we show that our learning-based approach for regular sampling improves reconstruction results compared to classical compressed sensing methods with variable density sampling for our target application.
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