  We perform a qualitative analysis of performance of XPDNet, a
state-of-the-art deep learning approach for MRI reconstruction, compared to
GRAPPA, a classical approach. We do this in multiple settings, in particular
testing the robustness of the XPDNet to unseen settings, and show that the
XPDNet can to some degree generalize well.
